Map generation and control system

ABSTRACT

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestrymachines, construction machines and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugar cane harvesters, cotton harvesters, self-propelledforage harvesters, and windrowers. Some harvesters can also be fittedwith different types of heads to harvest different types of crops.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

One or more information maps are obtained by an agricultural workmachine. The one or more information maps map one or more agriculturalcharacteristic values at different geographic locations of a field. Anin-situ sensor on the agricultural work machine senses an agriculturalcharacteristic as the agricultural work machine moves through the field.A predictive map generator generates a predictive map that predicts apredictive agricultural characteristic at different locations in thefield based on a relationship between the values in the one or moreinformation maps and the agricultural characteristic sensed by thein-situ sensor. The predictive map can be output and used in automatedmachine control.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to examples that solveany or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of oneexample of a combine harvester.

FIG. 2 is a block diagram showing some portions of an agriculturalharvester in more detail, according to some examples of the presentdisclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3 ) show a flowdiagram illustrating an example of operation of an agriculturalharvester in generating a map.

FIG. 4A is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 4B is a block diagram showing one example of the predictive modelgenerator in more detail.

FIG. 5 is a flow diagram showing an example of operation of anagricultural harvester in receiving a map, detecting a characteristicwith an in-situ sensor, and generating a functional predictive map forpresentation or use in controlling the agricultural harvester during aharvesting operation.

FIG. 6 is a block diagram of one example of a control zone generator.

FIG. 7 is a flow diagram showing one example of the operation of thecontrol zone generator.

FIG. 8 is a flow diagram showing one example of operation using controlzones.

FIG. 9 is a block diagram of one example of an operator interfacecontroller.

FIG. 10 is a flow diagram showing one example of operation of theoperator interface controller.

FIG. 11 is an illustration of one example of a user interface display.

FIG. 12 is a block diagram showing one example of an agriculturalharvester in communication with a remote server environment.

FIGS. 13-15 show examples of mobile devices that can be used in anagricultural harvester.

FIG. 16 is a block diagram showing one example of a computingenvironment that can be used in an agricultural harvester.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, and/or steps described with respect toone example may be combined with the features, components, and/or stepsdescribed with respect to other examples of the present disclosure.

The present description relates to using in-situ data taken concurrentlywith an agricultural operation, in combination with data from a map, togenerate a predictive map.

In some examples, a predictive map can be used to control anagricultural work machine, such as an agricultural harvester. Asdiscussed above, performance of an agricultural harvester may bedegraded or otherwise affected under different conditions. For instance,performance of a harvester (or other agricultural machine) may bedeleteriously affected based on the topography of a field. Thetopography can cause the machine to pitch and roll a certain amount whennavigating a side slope. Without limitation, machine pitch or roll canaffect the grain loss, internal material distribution, grain quality andtailings characteristics. For example, grain loss can be affected by atopographic characteristic that causes agricultural harvester 100 toeither pitch or roll. The increased pitch can cause grain to go out theback more quickly, decreased pitch can keep the grain in the machine,and the roll elements can overload the sides of the cleaning system anddrive up more grain loss on those sides. Similarly, grain quality can beimpacted by both pitch and roll, and similar to grain loss, thereactions of the material other than grain staying in the machine orleaving the machine based on the pitch or roll can be influential on thequality output. In another example, a topographic characteristicinfluencing pitch will have an impact on the amount of tailings enteringthe tailings system, thus impacting a tailings sensor output. Theconsideration of the pitch and the time at that level can have arelationship to how much tailings volume increases and could be usefulto estimate in the need to have controls for anticipating that level andmaking adjustments. In other examples, characteristics such as genotype,vegetative index, yield, biomass, and weed characteristics, such as weedtype or weed intensity, can affect other characteristics such astailings, crop loss, grain quality, and internal material distribution.

A topographic map illustratively maps elevations of the ground acrossdifferent geographic locations in a field of interest. Since groundslope is indicative of a change in elevation, having two or moreelevation values allows for calculation of slope across the areas havingknown elevation values. Greater granularity of slope can be accomplishedby having more areas with known elevation values. As an agriculturalharvester travels across the terrain in known directions, the pitch androll of the agricultural harvester can be determined based on the slopeof the ground (i.e., areas of changing elevation). Topographiccharacteristics, when referred to below, can include, but are notlimited to, the elevation, slope (e.g., including the machineorientation relative to the slope), and ground profile (e.g.,roughness).

In some examples, a predictive biomass map can be used to control anagricultural work machine, such as an agricultural harvester. Biomass,as used herein, refers to an amount of above ground vegetation materialin a given area or location. Often, the amount is measured in terms ofweight, for instance, weight per given area, such as tons per acre.Various characteristics can be indicative of biomass (referred to hereinas biomass characteristics) and can be used to predict the biomass on afield of interest. For example, biomass characteristics can includevarious crop characteristics, such as crop height (the height of thecrop above the surface of the field), crop density (the amount of cropmatter in a given space, which can be derived from the crop mass andcrop volume), crop mass (such as a weight of the crop or the weight ofcrop components), or crop volume (how much of the given area or locationis taken up by the crop, that is the space that the crop occupies orcontains). In another example, biomass characteristics can includevarious machine characteristics of the agricultural harvester, such asmachine settings or operating characteristics. For example, a force,such as a fluid pressure or torque, used to drive a threshing rotor ofthe agricultural harvester can be indicative of the biomass.

The performance of an agricultural harvester may be affected when theagricultural harvester engages areas of the field with variances inbiomass. For instance, if the machine settings of the agriculturalharvester are set on the basis of an expected or desired throughput, thevariance in biomass can cause the throughput to vary, and, thus, themachine settings can be suboptimal for effectively processing thevegetation, including the crop. As mentioned above, the operator canattempt to predict the biomass ahead of the machine. Additionally, somesystems, such as feedback control systems, reactively adjust the forwardground speed of the agricultural harvester in an attempt to maintain adesired throughput. This can be done by attempting to identify thebiomass based on sensor inputs, such as from sensors that sense avariable indicative of biomass. However, such arrangements can be proneto error and can be too slow to react to an upcoming change in biomassto effectively alter the operation of the machine to control throughput,such as by changing the forward speed of the harvester. For instance,such systems are typically reactive in that adjustments to the machinesettings are made only after the vegetation has been encountered by themachine in attempt to reduce further error, such as in a feedbackcontrol system.

Some current systems provide vegetative index maps. A vegetative indexmap illustratively maps vegetative index values (which may be indicativeof vegetative growth) across different geographic locations in a fieldof interest. One example of a vegetative index includes a normalizeddifference vegetation index (NDVI). There are many other vegetativeindices that are within the scope of the present disclosure. In someexamples, a vegetative index may be derived from sensor readings of oneor more bands of electromagnetic radiation reflected by the plants.Without limitations, these bands may be in the microwave, infrared,visible or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can be used to identify the presence and locationof vegetation. In some examples, a vegetative index map enables crops tobe identified and georeferenced in the presence of bare soil, cropresidue, or other plants, such as weeds. In other examples, a vegetativeindex map enables the detection of various crop characteristics, such ascrop growth and crop health or vigor, across different geographiclocations in a field of interest.

A seed genotype map maps the genotype (e.g., hybrid, cultivar, species,etc.) of seed planted at different locations in the field. The seedgenotype map can be generated by a planter or by a machine performing asubsequent operation, such as a sprayer with an optical detector thatdetects plant genotype.

A predictive yield map includes georeferenced predictive yield values.

A predictive weed map includes one or more of georeferenced predictiveweed characteristics, such as weed intensity values or weed type values.The weed intensity values may include, without limitation, at least oneof weed population, weed growth stage, weed size, weed biomass, weedmoisture, or weed health. The weed type values may include, withoutlimitation, an indication of weed type, such as an identification of theweed species.

The present discussion thus proceeds with respect to systems thatreceive at least one or more of a topographic map, a seed genotype map,a vegetative index map, a yield map, a biomass map, and a weed map andalso use an in-situ sensor to detect a value indicative of one or moreof an internal material distribution, grain loss or crop loss,characteristics of tailings, and grain quality, during a harvestingoperation. The systems generate a model that models one or morerelationships between the characteristics derived from the received mapsand the output values from the in-situ sensors. The one or more modelsare used to generate a functional predictive map that predicts acharacteristic such as a characteristic sensed by the one or morein-situ sensors or related characteristic at different geographiclocations in the field, based upon the one or more prior informationmaps. The functional predictive map, generated during the harvestingoperation, can be used in automatically controlling a harvester duringthe harvesting operation. The functional predictive map can also beprovided to an operator or another user as well.

FIG. 1 is a partial pictorial, partial schematic, illustration of aself-propelled agricultural harvester 100. In the illustrated example,agricultural harvester 100 is a combine harvester. Further, althoughcombine harvesters are provided as examples throughout the presentdisclosure, it will be appreciated that the present description is alsoapplicable to other types of harvesters, such as cotton harvesters,sugarcane harvesters, self-propelled forage harvesters, windrowers, orother agricultural work machines. Consequently, the present disclosureis intended to encompass the various types of harvesters described andis, thus, not limited to combine harvesters. Moreover, the presentdisclosure is directed to other types of work machines, such asagricultural seeders and sprayers, construction equipment, forestryequipment, and turf management equipment where generation of apredictive map may be applicable. Consequently, the present disclosureis intended to encompass these various types of harvesters and otherwork machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1 , agricultural harvester 100 illustratively includesan operator compartment 101, which can have a variety of differentoperator interface mechanisms, for controlling agricultural harvester100. Agricultural harvester 100 includes front-end equipment, such as aheader 102, and a cutter generally indicated at 104. Agriculturalharvester 100 also includes a feeder house 106, a feed accelerator 108,and a thresher generally indicated at 110. The feeder house 106 and thefeed accelerator 108 form part of a material handling subsystem 125.Header 102 is pivotally coupled to a frame 103 of agricultural harvester100 along pivot axis 105. One or more actuators 107 drive movement ofheader 102 about axis 105 in the direction generally indicated by arrow109. Thus, a vertical position of header 102 (the header height) aboveground 111 over which the header 102 travels is controllable byactuating actuator 107. While not shown in FIG. 1 , agriculturalharvester 100 may also include one or more actuators that operate toapply a tilt angle, a roll angle, or both to the header 102 or portionsof header 102. Tilt refers to an angle at which the cutter 104 engagesthe crop. The tilt angle is increased, for example, by controllingheader 102 to point a distal edge 113 of cutter 104 more toward theground. The tilt angle is decreased by controlling header 102 to pointthe distal edge 113 of cutter 104 more away from the ground. The rollangle refers to the orientation of header 102 about the front-to-backlongitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set ofconcaves 114. Further, agricultural harvester 100 also includes aseparator 116. Agricultural harvester 100 also includes a cleaningsubsystem or cleaning shoe (collectively referred to as cleaningsubsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve124. The material handling subsystem 125 also includes discharge beater126, tailings elevator 128, clean grain elevator 130, as well asunloading auger 134 and spout 136. The clean grain elevator moves cleangrain into clean grain tank 132. Agricultural harvester 100 alsoincludes a residue subsystem 138 that can include chopper 140 andspreader 142. Agricultural harvester 100 also includes a propulsionsubsystem that includes an engine that drives ground engaging components144, such as wheels or tracks. In some examples, a combine harvesterwithin the scope of the present disclosure may have more than one of anyof the subsystems mentioned above. In some examples, agriculturalharvester 100 may have left and right cleaning subsystems, separators,etc., which are not shown in FIG. 1 .

In operation, and by way of overview, agricultural harvester 100illustratively moves through a field in the direction indicated by arrow147. As agricultural harvester 100 moves, header 102 (and the associatedreel 164) engages the crop to be harvested and gathers the crop towardcutter 104. An operator of agricultural harvester 100 can be a localhuman operator, a remote human operator, or an automated system. Theoperator of agricultural harvester 100 may determine one or more of aheight setting, a tilt angle setting, or a roll angle setting for header102. For example, the operator inputs a setting or settings to a controlsystem, described in more detail below, that controls actuator 107. Thecontrol system may also receive a setting from the operator forestablishing the tilt angle and roll angle of the header 102 andimplement the inputted settings by controlling associated actuators, notshown, that operate to change the tilt angle and roll angle of theheader 102. The actuator 107 maintains header 102 at a height aboveground 111 based on a height setting and, where applicable, at desiredtilt and roll angles. Each of the height, roll, and tilt settings may beimplemented independently of the others. The control system responds toheader error (e.g., the difference between the height setting andmeasured height of header 104 above ground 111 and, in some examples,tilt angle and roll angle errors) with a responsiveness that isdetermined based on a sensitivity level. If the sensitivity level is setat a greater level of sensitivity, the control system responds tosmaller header position errors, and attempts to reduce the detectederrors more quickly than when the sensitivity is at a lower level ofsensitivity.

Returning to the description of the operation of agricultural harvester100, after crops are cut by cutter 104, the severed crop material ismoved through a conveyor in feeder house 106 toward feed accelerator108, which accelerates the crop material into thresher 110. The cropmaterial is threshed by rotor 112 rotating the crop against concaves114. The threshed crop material is moved by a separator rotor inseparator 116 where a portion of the residue is moved by dischargebeater 126 toward the residue subsystem 138. The portion of residuetransferred to the residue subsystem 138 is chopped by residue chopper140 and spread on the field by spreader 142. In other configurations,the residue is released from the agricultural harvester 100 in awindrow. In other examples, the residue subsystem 138 can include weedseed eliminators (not shown) such as seed baggers or other seedcollectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some largerpieces of material from the grain, and sieve 124 separates some of finerpieces of material from the clean grain. Clean grain falls to an augerthat moves the grain to an inlet end of clean grain elevator 130, andthe clean grain elevator 130 moves the clean grain upwards, depositingthe clean grain in clean grain tank 132. Residue is removed from thecleaning subsystem 118 by airflow generated by cleaning fan 120.Cleaning fan 120 directs air along an airflow path upwardly through thesieves and chaffers. The airflow carries residue rearwardly inagricultural harvester 100 toward the residue handling subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where thetailings are re-threshed. Alternatively, the tailings also may be passedto a separate re-threshing mechanism by a tailings elevator or anothertransport device where the tailings are re-threshed as well.

While not shown in FIG. 1 , agricultural harvester 100 can, in someexamples, include one or more adjustable material engaging elementsdisposed in the material flow path within agricultural harvester 100.These adjustable material engaging elements can include, withoutlimitation, blades, such as rudder blades, or other adjustable members,that can be adjustably moved (e.g., angled, pivoted, etc.) to directmaterial within the flow path. The adjustable material engaging elementsmay direct at least a portion of the material stream right or leftrelative to the direction of flow, such as to a left or right cleaningsubsystem, a left or right separator, or various other components andsubsystems of agricultural harvester that may include both a left and aright, as described above. In some examples, the direction may be fromareas of greater material depth to areas of less material depthlaterally or fore and aft relative to the direction of material flow.These adjustable material engaging elements can be controlled via anactuator (e.g., hydraulic, electric, pneumatic, etc.) to controlmaterial distribution within agricultural harvester 100.

FIG. 1 also shows that, in one example, agricultural harvester 100includes ground speed sensor 146, one or more separator loss sensors148, a clean grain camera 150, a forward looking image capture mechanism151, which may be in the form of a stereo or mono camera, and one ormore loss sensors 152 provided in the cleaning subsystem 118.

Ground speed sensor 146 senses the travel speed of agriculturalharvester 100 over the ground. Ground speed sensor 146 may sense thetravel speed of the agricultural harvester 100 by sensing the speed ofrotation of the ground engaging components (such as wheels or tracks), adrive shaft, an axle, or other components. In some instances, the travelspeed may be sensed using a positioning system, such as a globalpositioning system (GPS), a dead reckoning system, a long rangenavigation (LORAN) system, a Doppler speed sensor, or a wide variety ofother systems or sensors that provide an indication of travel speed.Ground speed sensors 146 can also include direction sensors such as acompass, a magnetometer, a gravimetric sensor, a gyroscope, GPSderivation, to determine the direction of travel in two or threedimensions in combination with the speed. This way, when agriculturalharvester 100 is on a slope, the orientation of agricultural harvester100 relative to the slope is known. For example, an orientation ofagricultural harvester 100 could include ascending, descending ortransversely travelling the slope. Machine or ground speed, whenreferred to in this disclosure can also include the two or threedimension direction of travel.

Loss sensors 152 illustratively provide an output signal indicative ofthe quantity of grain loss occurring in both the right and left sides ofthe cleaning subsystem 118. In some examples, sensors 152 are strikesensors which count grain strikes per unit of time or per unit ofdistance traveled to provide an indication of the grain loss occurringat the cleaning subsystem 118. The strike sensors for the right and leftsides of the cleaning subsystem 118 may provide individual signals or acombined or aggregated signal. In some examples, sensors 152 may includea single sensor as opposed to separate sensors provided for eachcleaning subsystem 118.

Separator loss sensor 148 provides a signal indicative of grain loss inthe left and right separators, not separately shown in FIG. 1 . Theseparator loss sensors 148 may be associated with the left and rightseparators and may provide separate grain loss signals or a combined oraggregate signal. In some instances, sensing grain loss in theseparators may also be performed using a wide variety of different typesof sensors as well.

Agricultural harvester 100 may also include other sensors andmeasurement mechanisms. For instance, agricultural harvester 100 mayinclude one or more of the following sensors: a header height sensorthat senses a height of header 102 above ground 111; stability sensorsthat sense oscillation or bouncing motion (and amplitude) ofagricultural harvester 100; a residue setting sensor that is configuredto sense whether agricultural harvester 100 is configured to chop theresidue, produce a windrow, etc.; a cleaning shoe fan speed sensor tosense the speed of fan 120; a concave clearance sensor that sensesclearance between the rotor 112 and concaves 114; a threshing rotorspeed sensor that senses a rotor speed of rotor 112; a chaffer clearancesensor that senses the size of openings in chaffer 122; a sieveclearance sensor that senses the size of openings in sieve 124; amaterial other than grain (MOG) moisture sensor that senses a moisturelevel of the MOG passing through agricultural harvester 100; one or moremachine setting sensors configured to sense various configurablesettings of agricultural harvester 100; a machine orientation sensorthat senses the orientation of agricultural harvester 100; and cropproperty sensors that sense a variety of different types of cropproperties, such as crop type, crop moisture, and other crop properties.Crop property sensors may also be configured to sense characteristics ofthe severed crop material as the crop material is being processed byagricultural harvester 100. For example, in some instances, the cropproperty sensors may sense grain quality such as broken grain, MOGlevels; grain constituents such as starches and protein; and grain feedrate as the grain travels through the feeder house 106, clean grainelevator 130, or elsewhere in the agricultural harvester 100. The cropproperty sensors may also sense the feed rate of biomass through feederhouse 106, through the separator 116 or elsewhere in agriculturalharvester 100. The crop property sensors may also sense the feed rate asa mass flow rate of grain through elevator 130 or through other portionsof the agricultural harvester 100 or provide other output signalsindicative of other sensed variables. An internal material distributionsensor may sense material distribution internal to agriculturalharvester 100.

Examples of sensors used to detect or sense the power characteristicsinclude, but are not limited to, a voltage sensor, a current sensor, atorque sensor, a hydraulic pressure sensor, a hydraulic flow sensor, aforce sensor, a bearing load sensor and a rotational sensor. Powercharacteristics can be measured at varying levels of granularity. Forinstance, power usage can be sensed machine-wide, subsystem-wide or byindividual components of the subsystems.

Examples of sensors used to detect internal material distributioninclude, but are not limited to, one or more cameras, capacitivesensors, electromagnetic or ultrasonic time-of-flight reflectivesensors, signal attenuation sensors, weight or mass sensors, materialflow sensors, etc. These sensors can be placed at one or more locationsin agricultural harvester 100 to sense the distribution of the materialin agricultural harvester 100, during the operation of agriculturalharvester 100.

Examples of sensors used to detect or sense a pitch or roll ofagricultural harvester 100 include accelerometers, gyroscopes, inertialmeasurement units, gravimetric sensors, magnetometers, etc. Thesesensors can also be indicative of the slope of the terrain thatagricultural harvester 100 is currently on.

Prior to describing how agricultural harvester 100 generates afunctional predictive map, and uses the functional predictive map forcontrol, a brief description of some of the items on agriculturalharvester 100, and their operation, will first be described. Thedescription of FIGS. 2 and 3 describe receiving a general type of priorinformation map and combining information from the prior information mapwith a georeferenced sensor signal generated by an in-situ sensor, wherethe sensor signal may be indicative of an agricultural characteristic,such as one or more of a characteristic in the field, characteristics ofcrop properties, characteristics of grain, or characteristics ofagricultural harvester 100. Characteristics of the “field” may include,but are not limited to, characteristics of a field such as slope, weedcharacteristics (such as weed intensity or weed type), soil moisture,and surface quality. Characteristics of crop properties may include,without limitation, crop height, crop moisture, grain quality, cropdensity, and crop state. Characteristics of grain may include, withoutlimitations, grain moisture, grain size, grain test weight; andcharacteristics of agricultural harvester 100 may include, withoutlimitation, orientation, loss levels, job quality, fuel consumption,internal material distribution, tailings characteristics, and powerutilization. A relationship between the characteristic values obtainedfrom in-situ sensor signals and the prior information map values isidentified, and that relationship is used to generate a new functionalpredictive map 263. A functional predictive map 263 predicts values atdifferent geographic locations in a field, and one or more of thosevalues can be used for controlling a machine. In some instances, afunctional predictive map 263 can be presented to a user, such as anoperator of an agricultural work machine, which may be an agriculturalharvester. A functional predictive map 263 can be presented to a uservisually, such as via a display, haptically, or audibly. The user caninteract with the functional predictive map 263 to perform editingoperations and other user interface operations. In some instances, afunctional predictive map both can be used for controlling anagricultural work machine, such as an agricultural harvester,presentation to an operator or other user, and presentation to anoperator or user for interaction by the operator or user.

After the general approach is described with respect to FIGS. 2 and 3 ,a more specific approach for generating a functional predictive map 263that can be presented to an operator or user, or used to controlagricultural harvester 100, or both is described with respect to FIGS. 4and 5 . Again, while the present discussion proceeds with respect to theagricultural harvester and, particularly, a combine harvester, the scopeof the present disclosure encompasses other types of agriculturalharvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester 100. FIG. 2 shows that agricultural harvester 100illustratively includes one or more processors or servers 201, datastore 202, geographic position sensor 204, communication system 206, andone or more in-situ sensors 208 that sense one or more agriculturalcharacteristics concurrent with a harvesting operation. An agriculturalcharacteristic can include any characteristic that can have an effect onthe harvesting operation. Some examples of agricultural characteristicsinclude characteristics of the agricultural harvester, the field, theplants on the field, and the weather. Other types of agriculturalcharacteristics are also included. The in-situ sensors 208 generatevalues corresponding to the sensed characteristics. The agriculturalharvester 100 also includes a predictive model or relationship generator(collectively referred to hereinafter as “predictive model generator210”), predictive map generator 212, control zone generator 213, controlsystem 214, one or more controllable subsystems 216, and an operatorinterface mechanism 218. The agricultural harvester 100 can also includea wide variety of other agricultural harvester functionality 220. Thein-situ sensors 208 include, for example, on-board sensors 222, remotesensors 224, and other sensors 226 that sense characteristics during thecourse of an agricultural operation. Predictive model generator 210illustratively includes a prior information variable-to-in-situ variablemodel generator 228, and predictive model generator 210 can includeother items 230. Control system 214 includes communication systemcontroller 229, operator interface controller 231, a settings controller232, path planning controller 234, feed rate controller 236, header andreel controller 238, draper belt controller 240, deck plate positioncontroller 242, residue system controller 244, machine cleaningcontroller 245, zone controller 247, and system 214 can include otheritems 246. Controllable subsystems 216 include machine and headeractuators 248, propulsion subsystem 250, steering subsystem 252, residuesubsystem 138, machine cleaning subsystem 254, and subsystems 216 caninclude a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receive priorinformation map 258. As described below, the prior map information map258 includes, for example, a topographic map from a prior operation inthe field, such as an unmanned aerial vehicle completing a rangescanning operation from a known altitude, a topographic map sensed by aplane, a topographic map sensed by a satellite, a topographic map sensedby a ground vehicle, such as a GPS-equipped planter, etc. Priorinformation map 258 can also include one or more of a seed genotype map,a vegetative index (VI) map, a yield map, a biomass map, or a weed map.However, prior map information may also encompass other types of datathat were obtained prior to a harvesting operation or a map from a prioroperation. For instance, a topographic map can be retrieved from aremote source such as the United States Geological Survey (USGS). FIG. 2also shows that an operator 260 may operate the agricultural harvester100. The operator 260 interacts with operator interface mechanisms 218.In some examples, operator interface mechanisms 218 may includejoysticks, levers, a steering wheel, linkages, pedals, buttons, dials,keypads, user actuatable elements (such as icons, buttons, etc.) on auser interface display device, a microphone and speaker (where speechrecognition and speech synthesis are provided), among a wide variety ofother types of control devices. Where a touch sensitive display systemis provided, operator 260 may interact with operator interfacemechanisms 218 using touch gestures. These examples described above areprovided as illustrative examples and are not intended to limit thescope of the present disclosure. Consequently, other types of operatorinterface mechanisms 218 may be used and are within the scope of thepresent disclosure.

Prior information map 258 may be downloaded onto agricultural harvester100 and stored in data store 202, using communication system 206 or inother ways. In some examples, communication system 206 may be a cellularcommunication system, a system for communicating over a wide areanetwork or a local area network, a system for communicating over a nearfield communication network, or a communication system configured tocommunicate over any of a variety of other networks or combinations ofnetworks. Communication system 206 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card or both.

Geographic position sensor 204 illustratively senses or detects thegeographic position or location of agricultural harvester 100.Geographic position sensor 204 can include, but is not limited to, aglobal navigation satellite system (GNSS) receiver that receives signalsfrom a GNSS satellite transmitter. Geographic position sensor 204 canalso include a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensor 204 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

In-situ sensors 208 may be any of the sensors described above withrespect to FIG. 1 . In-situ sensors 208 include on-board sensors 222that are mounted on-board agricultural harvester 100. Such sensors mayinclude, for instance, a speed sensor (e.g., a GPS, speedometer, orcompass), image sensors that are internal to agricultural harvester 100(such as the clean grain camera or cameras mounted to identify materialdistribution in agricultural harvester 100, for example, in the residuesubsystem or the cleaning system), grain loss sensors, tailingcharacteristic sensors, and grain quality sensors. The in-situ sensors208 also include remote in-situ sensors 224 that capture in-situinformation. In-situ data include data taken from a sensor on-board theharvester or taken by any sensor where the data are detected during theharvesting operation.

Predictive model generator 210 generates a model that is indicative of arelationship between the values sensed by the in-situ sensor 208 and acharacteristic mapped to the field by the prior information map 258. Forexample, if the prior information map 258 maps a topographiccharacteristic to different locations in the field, and the in-situsensor 208 is sensing a value indicative of internal materialdistribution, then prior information variable-to-in-situ variable modelgenerator 228 generates a predictive model that models the relationshipbetween the topographic characteristics and the internal materialdistribution. The predictive machine model can also be generated basedon characteristics from one or more of the prior information maps 258and one or more in-situ data values generated by in-situ sensors 208.Then, predictive map generator 212 uses the predictive model generatedby predictive model generator 210 to generate a functional predictivemap 263 that predicts the value of a characteristic, such as internalmaterial distribution, tailings characteristics, loss, or grain quality,sensed by the in-situ sensors 208 at different locations in the fieldbased upon the prior information map 258.

In some examples, the type of values in the functional predictive map263 may be the same as the in-situ data type sensed by the in-situsensors 208. In some instances, the type of values in the functionalpredictive map 263 may have different units from the data sensed by thein-situ sensors 208. In some examples, the type of values in thefunctional predictive map 263 may be different from the data type sensedby the in-situ sensors 208 but have a relationship to the type of datatype sensed by the in-situ sensors 208. For example, in some examples,the data type sensed by the in-situ sensors 208 may be indicative of thetype of values in the functional predictive map 263. In some examples,the type of data in the functional predictive map 263 may be differentthan the data type in the prior information map 258. In some instances,the type of data in the functional predictive map 263 may have differentunits from the data in the prior information map 258. In some examples,the type of data in the functional predictive map 263 may be differentfrom the data type in the prior information map 258 but has arelationship to the data type in the prior information map 258. Forexample, in some examples, the data type in the prior information map258 may be indicative of the type of data in the functional predictivemap 263. In some examples, the type of data in the functional predictivemap 263 is different than one of, or both of, the in-situ data typesensed by the in-situ sensors 208 and the data type in the priorinformation map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of, or both of, thein-situ data type sensed by the in-situ sensors 208 and the data type inprior information map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of the in-situ datatype sensed by the in-situ sensors 208 or the data type in the priorinformation map 258, and different than the other.

Predictive map generator 212 can use the characteristics in priorinformation map 258, and the model generated by predictive modelgenerator 210, to generate a functional predictive map 263 that predictsthe characteristics at different locations in the field. Predictive mapgenerator 212 thus outputs predictive map 264.

As shown in FIG. 2 , predictive map 264 predicts the value of a sensedcharacteristic (sensed by in-situ sensors 208), or a characteristicrelated to the sensed characteristic, at various locations across thefield based upon a prior information value in prior information map 258at those locations and using the predictive model. For example, ifpredictive model generator 210 has generated a predictive modelindicative of a relationship between a topographic characteristic andgrain quality, then, given the topographic characteristics at differentlocations across the field, predictive map generator 212 generates apredictive map 264 that predicts the value of the grain quality atdifferent locations across the field. The topographic characteristic,obtained from the topographic map, at those locations and therelationship between topographic characteristic and grain qualitycharacteristic, obtained from the predictive model, are used to generatethe predictive map 264. The predicted grain quality can be used by acontrol system to adjust, for example, one or more of sieve and chafferopenings, rotor operation, concave clearance (i.e., the space betweenthe threshing rotor and the concave), or cleaning fan speed.

Some variations in the data types that are mapped in the priorinformation map 258, the data types sensed by in-situ sensors 208 andthe data types predicted on the predictive map 264 will now bedescribed. These are only examples to illustrate that the data types canbe the same or different.

In some examples, the data type in the prior information map 258 isdifferent from the data type sensed by in-situ sensors 208, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 208. For instance, the prior information map 258 maybe a topographic map, and the variable sensed by the in-situ sensors 208may be a grain quality characteristic. The predictive map 264 may thenbe a predictive machine map that maps predicted machine characteristicvalues to different geographic locations in the field.

Also, in some examples, the data type in the prior information map 258is different from the data type sensed by in-situ sensors 208, and thedata type in the predictive map 264 is different from both the data typein the prior information map 258 and the data type sensed by the in-situsensors 208. For instance, the prior information map 258 may be atopographic map, and the variable sensed by the in-situ sensors 208 maybe machine pitch/roll. The predictive map 264 may then be a predictiveinternal distribution map that maps predicted internal distributionvalues to different geographic locations in the field.

In some examples, the prior information map 258 is from a prioroperation through the field and the data type is different from the datatype sensed by in-situ sensors 208, yet the data type in the predictivemap 264 is the same as the data type sensed by the in-situ sensors 208.For instance, the prior information map 258 may be a seed genotype mapgenerated during planting, and the variable sensed by the in-situsensors 208 may be loss. The predictive map 264 may then be a predictiveloss map that maps predicted grain loss values to different geographiclocations in the field. In another example, the prior information map258 may be a seeding genotype map, and the variable sensed by thein-situ sensors 208 may be crop state such as standing crop or downcrop. The predictive map 264 may then be a predictive crop state mapthat maps predicted crop state values to different geographic locationsin the field.

In some examples, the prior information map 258 is from a prioroperation through the field and the data type is the same as the datatype sensed by in-situ sensors 208, and the data type in the predictivemap 264 is also the same as the data type sensed by the in-situ sensors208. For instance, the prior information map 258 may be a yield mapgenerated during a previous year, and the variable sensed by the in-situsensors 208 may be yield. The predictive map 264 may then be apredictive yield map that maps predicted yield values to differentgeographic locations in the field. In such an example, the relativeyield differences in the georeferenced prior information map 258 fromthe prior year can be used by predictive model generator 210 to generatea predictive model that models a relationship between the relative yielddifferences on the prior information map 258 and the yield values sensedby in-situ sensors 208 during the current harvesting operation. Thepredictive model is then used by predictive map generator 210 togenerate a predictive yield map.

In some examples, predictive map 264 can be provided to the control zonegenerator 213. Control zone generator 213 groups contiguous individualpoint data values on predictive map 264, into control zones. A controlzone may include two or more contiguous portions of an area, such as afield, for which a control parameter corresponding to the control zonefor controlling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 216 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator213 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems216. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 216 or for groups of controllable subsystems 216.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265. Insome examples, multiple crops may be simultaneously present in a fieldif an intercrop production system is implemented. In that case,predictive map generator 212 and control zone generator 213 are able toidentify the location and characteristics of the two or more crops andthen generate predictive map 264 and predictive control zone map 265accordingly.

It will also be appreciated that control zone generator 213 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay only be used for controlling or calibrating agricultural harvester100 or both. In other examples, the control zones may be presented tothe operator 260 and used to control or calibrate agricultural harvester100 and in other examples the control zones may just be presented to theoperator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 214, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 229 controlscommunication system 206 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to otheragricultural harvesters that are harvesting in the same field. In someexamples, communication system controller 229 controls the communicationsystem 206 to send the predictive map 264, predictive control zone map265, or both to other remote systems.

In some examples, predictive map 264 can be provided to route/missiongenerator 267. Route/mission generator 267 plots a travel path foragricultural harvester 100 to travel on during the harvesting operationbased on predictive map 264. The travel path can also include machinecontrol settings corresponding to locations along the travel path aswell. For example, if a travel path ascends a hill, then at a pointprior to hill ascension, the travel path can include a controlindicative of directing power to propulsion systems to maintain a speedor feed rate of agricultural harvester 100. In some examples,route/mission generator 267 analyzes the different orientations ofagricultural harvester 100 and the predicted machine characteristicsthat the orientations are predicted to generate according to predictivemap 264, for a plurality of different travel routes, and selects a routethat has desirable results (such as, quick harvest time or desired powerutilization or material distribution uniformity).

Operator interface controller 231 is operable to generate controlsignals to control operator interface mechanisms 218. The operatorinterface controller 231 is also operable to present the predictive map264 or predictive control zone map 265 or other information derived fromor based on the predictive map 264, predictive control zone map 265, orboth to operator 260. Operator 260 may be a local operator or a remoteoperator. As an example, controller 231 generates control signals tocontrol a display mechanism to display one or both of predictive map 264and predictive control zone map 265 for the operator 260. Controller 231may generate operator actuatable mechanisms that are displayed and canbe actuated by the operator to interact with the displayed map. Theoperator can edit the map by, for example, correcting a powerutilization displayed on the map, based on the operator's observation.Settings controller 232 can generate control signals to control varioussettings on the agricultural harvester 100 based upon predictive map264, the predictive control zone map 265, or both. For instance,settings controller 232 can generate control signals to control machineand header actuators 248. In response to the generated control signals,the machine and header actuators 248 operate to control, for example,one or more of the sieve and chaffer settings, concave clearance, rotorsettings, cleaning fan speed settings, header height, headerfunctionality, reel speed, reel position, draper functionality (whereagricultural harvester 100 is coupled to a draper header), corn headerfunctionality, internal distribution control and other actuators 248that affect the other functions of the agricultural harvester 100. Pathplanning controller 234 illustratively generates control signals tocontrol steering subsystem 252 to steer agricultural harvester 100according to a desired path. Path planning controller 234 can control apath planning system to generate a route for agricultural harvester 100and can control propulsion subsystem 250 and steering subsystem 252 tosteer agricultural harvester 100 along that route. Feed rate controller236 can control various subsystems, such as propulsion subsystem 250 andmachine actuators 248, to control a feed rate based upon the predictivemap 264 or predictive control zone map 265 or both. For instance, asagricultural harvester 100 approaches a declining terrain having anestimated speed value above a selected threshold, feed rate controller236 may reduce the speed of machine 100 to maintain constant feed rateof biomass through the agricultural harvester 100. Header and reelcontroller 238 can generate control signals to control a header or areel or other header functionality. Draper belt controller 240 cangenerate control signals to control a draper belt or other draperfunctionality based upon the predictive map 264, predictive control zonemap 265, or both. For example, as agricultural harvester 100 approachesa declining terrain having an estimated speed value above a selectedthreshold, draper belt controller 240 may increase the speed of thedraper belts to prevent backup of material on the belts. Deck plateposition controller 242 can generate control signals to control aposition of a deck plate included on a header based on predictive map264 or predictive control zone map 265 or both, and residue systemcontroller 244 can generate control signals to control a residuesubsystem 138 based upon predictive map 264 or predictive control zonemap 265, or both. Machine cleaning controller 245 can generate controlsignals to control machine cleaning subsystem 254. For instance, asagricultural harvester 100 is about to transversely travel on a slopewhere it is estimated that the internal material distribution will bedisproportionally on one side of cleaning subsystem 254, machinecleaning controller 245 can adjust cleaning subsystem 254 to accountfor, or correct, the disproportionate material. Other controllersincluded on the agricultural harvester 100 can control other subsystemsbased on the predictive map 264 or predictive control zone map 265 orboth as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3 ) show a flowdiagram illustrating one example of the operation of agriculturalharvester 100 in generating a predictive map 264 and predictive controlzone map 265 based upon prior information map 258.

At 280, agricultural harvester 100 receives prior information map 258.Examples of prior information map 258 or receiving prior information map258 are discussed with respect to blocks 281, 282, 284 and 286. Asdiscussed above, prior information map 258 maps values of a variable,corresponding to a first characteristic, to different locations in thefield, as indicated at block 282. As indicated at block 281, receivingthe prior information map 258 may involve selecting one or more of aplurality of possible prior information maps that are available. Forinstance, one prior information map may be a terrain profile mapgenerated from aerial phase profilometry imagery. Another priorinformation map may be a map generated during a prior pass through thefield which may have been performed by a different machine performing aprevious operation in the field, such as a sprayer or other machine. Theprocess by which one or more prior information maps are selected can bemanual, semi-automated or automated. The prior information map 258 isbased on data collected prior to a current harvesting operation. This isindicated by block 284. For instance, the data may be collected by a GPSreceiver mounted on a piece of equipment during a prior field operation.For instance, the data may be collected in a lidar range scanningoperation during a previous year, or earlier in the current growingseason, or at other times. The data may be based on data detected orreceived in ways other than using lidar range scanning. For instance, adrone equipped with a fringe projection profilometry system may detectthe profile or elevation of the terrain. Or for instance, sometopographic features can be estimated based on weather patterns, such asthe formation of ruts due to erosion or the breakup of clumps overfreeze-thaw cycles. In some examples, prior information map 258 may becreated by combining data from a number of sources such as those listedabove. Or for instance, the data for the prior information map 258, suchas a topographic map can be transmitted to agricultural harvester 100using communication system 206 and stored in data store 202. The datafor the prior information map 258 can be provided to agriculturalharvester 100 using communication system 206 in other ways as well, andthis is indicated by block 286 in the flow diagram of FIG. 3 . In someexamples, the prior information map 258 can be received by communicationsystem 206.

Upon commencement of a harvesting operation, in-situ sensors 208generate sensor signals indicative of one or more in-situ data valuesindicative of a machine characteristic, for example, power usage,machine speed, internal material distribution, grain loss, tailingscharacteristics (such as tailings level, tailings flow, tailings volume,and tailings composition), or grain quality. Examples of in-situ sensors208 are discussed with respect to blocks 222, 290, and 226. As explainedabove, the in-situ sensors 208 include on-board sensors 222; remotein-situ sensors 224, such as UAV-based sensors flown at a time to gatherin-situ data, shown in block 290; or other types of in-situ sensors,designated by in-situ sensors 226. In some examples, data from on-boardsensors is georeferenced using position, heading or speed data fromgeographic position sensor 204.

Predictive model generator 210 controls the prior informationvariable-to-in-situ variable model generator 228 to generate a modelthat models a relationship between the mapped values contained in theprior information map 258 and the in-situ values sensed by the in-situsensors 208 as indicated by block 292. The characteristics or data typesrepresented by the mapped values in the prior information map 258 andthe in-situ values sensed by the in-situ sensors 208 may be the samecharacteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 isprovided to predictive map generator 212. Predictive map generator 212generates a predictive map 264 that predicts a value of thecharacteristic sensed by the in-situ sensors 208 at different geographiclocations in a field being harvested, or a different characteristic thatis related to the characteristic sensed by the in-situ sensors 208,using the predictive model and the prior information map 258, asindicated by block 294.

It should be noted that, in some examples, the prior information map 258may include two or more different maps or two or more different maplayers of a single map. Each map in the two or more different maps oreach layer in the two or more different map layers of a single map, mapa different type of variable to the geographic locations in the field.In such an example, predictive model generator 210 generates apredictive model that models the relationship between the in-situ dataand each of the different variables mapped by the two or more differentmaps or the two or more different map layers. Similarly, the in-situsensors 208 can include two or more sensors each sensing a differenttype of variable. Thus, the predictive model generator 210 generates apredictive model that models the relationships between each type ofvariable mapped by the prior information map 258 and each type ofvariable sensed by the in-situ sensors 208. Predictive map generator 212can generate a functional predictive map that predicts a value for eachsensed characteristic sensed by the in-situ sensors 208 (or acharacteristic related to the sensed characteristic) at differentlocations in the field being harvested using the predictive model andeach of the maps or map layers in the prior information map 258.

Predictive map generator 212 configures the predictive map 264 so thatthe predictive map 264 is actionable (or consumable) by control system214. Predictive map generator 212 can provide the predictive map 264 tothe control system 214 or to control zone generator 213 or both. Someexamples of different ways in which the predictive map 264 can beconfigured or output are described with respect to blocks 296, 293, 295,299 and 297. For instance, predictive map generator 212 configurespredictive map 264 so that predictive map 264 includes values that canbe read by control system 214 and used as the basis for generatingcontrol signals for one or more of the different controllable subsystemsof the agricultural harvester 100, as indicated by block 296.

Route/mission generator 267 plots a travel path for agriculturalharvester 100 to travel on during the harvesting operation based onpredictive map 204, as indicated by block 293. Control zone generator213 can divide the predictive map 264 into control zones based on thevalues on the predictive map 264. Contiguously-geolocated values thatare within a threshold value of one another can be grouped into acontrol zone. The threshold value can be a default threshold value, orthe threshold value can be set based on an operator input, based on aninput from an automated system or based on other criteria. A size of thezones may be based on a responsiveness of the control system 214, thecontrollable subsystems 216, or based on wear considerations, or onother criteria as indicated by block 295. Predictive map generator 212configures predictive map 264 for presentation to an operator or otheruser. Control zone generator 213 can configure predictive control zonemap 265 for presentation to an operator or other user. This is indicatedby block 299. When presented to an operator or other user, thepresentation of the predictive map 264 or predictive control zone map265 or both may contain one or more of the predictive values on thepredictive map 264 correlated to geographic location, the control zoneson predictive control zone map 265 correlated to geographic location,and settings values or control parameters that are used based on thepredicted values on predictive map 264 or zones on predictive controlzone map 265. The presentation can, in another example, include moreabstracted information or more detailed information. The presentationcan also include a confidence level that indicates an accuracy withwhich the predictive values on predictive map 264 or the zones onpredictive control zone map 265 conform to measured values that may bemeasured by sensors on agricultural harvester 100 as agriculturalharvester 100 moves through the field. Further where information ispresented to more than one location, an authentication or authorizationsystem can be provided to implement authentication and authorizationprocesses. For instance, there may be a hierarchy of individuals thatare authorized to view and change maps and other presented information.By way of example, an on-board display device may show the maps in nearreal time locally on the machine, only, or the maps may also begenerated at one or more remote locations. In some examples, eachphysical display device at each location may be associated with a personor a user permission level. The user permission level may be used todetermine which display elements are visible on the physical displaydevice, and which values the corresponding person may change. As anexample, a local operator of machine 100 may be unable to see theinformation corresponding to the predictive map 264 or make any changesto machine operation. A supervisor, at a remote location, however, maybe able to see the predictive map 264 on the display, but not makechanges. A manager, who may be at a separate remote location, may beable to see all of the elements on predictive map 264 and also changethe predictive map 264 that is used in machine control. This is oneexample of an authorization hierarchy that may be implemented. Thepredictive map 264 or predictive control zone map 265 or both can beconfigured in other ways as well, as indicated by block 297.

At block 298, input from geographic position sensor 204 and otherin-situ sensors 208 are received by the control system. Block 300represents receipt by control system 214 of an input from the geographicposition sensor 204 identifying a geographic location of agriculturalharvester 100. Block 302 represents receipt by the control system 214 ofsensor inputs indicative of trajectory or heading of agriculturalharvester 100, and block 304 represents receipt by the control system214 of a speed of agricultural harvester 100. Block 306 representsreceipt by the control system 214 of other information from variousin-situ sensors 208.

At block 308, control system 214 generates control signals to controlthe controllable subsystems 216 based on the predictive map 264 orpredictive control zone map 265 or both and the input from thegeographic position sensor 204 and any other in-situ sensors 208. Atblock 310, control system 214 applies the control signals to thecontrollable subsystems. It will be appreciated that the particularcontrol signals that are generated, and the particular controllablesubsystems 216 that are controlled, may vary based upon one or moredifferent things. For example, the control signals that are generatedand the controllable subsystems 216 that are controlled may be based onthe type of predictive map 264 or predictive control zone map 265 orboth that is being used. Similarly, the control signals that aregenerated and the controllable subsystems 216 that are controlled andthe timing of the control signals can be based on various latencies ofcrop flow through the agricultural harvester 100 and the responsivenessof the controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of afunctional predictive crop loss map can be used to control one or moresubsystems 216. For instance, the functional predictive loss map caninclude crop loss values georeferenced to locations within the fieldbeing harvested. The crop loss values from the functional predictiveloss map can be extracted and used to control the fan speed to ensurethe cleaning fan 120 minimizes crop loss through the cleaning subsystem118 as agricultural harvester 100 moves through the field. The precedingexample involving using a predictive crop loss map is provided merely asan example. Consequently, a wide variety of other control signals can begenerated using values obtained from a predictive machine map or othertype of predictive map to control one or more of the controllablesubsystems 216.

At block 312, a determination is made as to whether the harvestingoperation has been completed. If harvesting is not completed theprocessing advances to block 314 where in-situ sensor data fromgeographic position sensor 204 and in-situ sensors 208 (and perhapsother sensors) continues to be read.

In some examples, at block 316, agricultural harvester 100 can alsodetect learning trigger criteria to perform machine learning on one ormore of the predictive map 264, predictive control zone map 265, themodel generated by predictive model generator 210, the zones generatedby control zone generator 213, one or more control algorithmsimplemented by the controllers in the control system 214, and othertriggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 318, 320, 321, 322 and 324. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data is obtained from in-situ sensors 208. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 208 that exceeds a threshold trigger or causes thepredictive model generator 210 to generate a new predictive model thatis used by predictive map generator 212. Thus, as agricultural harvester100 continues a harvesting operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 208 triggers the creationof a new relationship represented by a predictive model generated bypredictive model generator 210. Further, new predictive map 264,predictive control zone map 265, or both can be regenerated using thenew predictive model. Block 318 represents detecting a threshold amountof in-situ sensor data used to trigger creation of a new predictivemodel.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 208 are changingfrom previous values or from a threshold value. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in prior information map258) is within a range, is less than a defined amount, or below athreshold value, then a new predictive model is not generated by thepredictive model generator 210. As a result, the predictive mapgenerator 212 does not generate a new predictive map 264, predictivecontrol zone map 265, or both. However, if variations within the in-situsensor data exceed the range or exceed the predefined amount or thethreshold value, for example, or if a relationship between the in-situsensor data and the information in prior information map 258 varies by adefined amount, for example, then the predictive model generator 210generates a new predictive model using all or a portion of the newlyreceived in-situ sensor data that the predictive map generator 212 usesto generate a new predictive map 264. At block 320, variations in thein-situ sensor data, such as a magnitude of an amount by which the dataexceeds the selected range or a magnitude of the variation of therelationship between the in-situ sensor data and the information in theprior information map 258, can be used as a trigger to cause generationof a new predictive model and predictive map. The threshold, the rangeand the defined amount can be set to default values, or set by anoperator or user interaction through a user interface, or set by anautomated system or in other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 210 switches to a different prior informationmap (different from the originally selected prior information map 258),then switching to the different prior information map may triggerrelearning by predictive model generator 210, predictive map generator212, control zone generator 213, control system 214, or other items. Inanother example, transitioning of agricultural harvester 100 to adifferent topography or to a different control zone may be used aslearning trigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 orpredictive control zone map 265 or both. The edits can change a value onthe predictive map 264 or, change the size, shape, position or existenceof a control zone, or a value on predictive control zone map 265 orboth. Block 321 shows that edited information can be used as learningtrigger criteria.

In some instances, it may also be that operator 260 observes thatautomated control of a controllable subsystem, is not what the operatordesires. In such instances, the operator 260 may provide anoperator-initiated adjustment to the controllable subsystem reflectingthat the operator 260 desires the controllable subsystem to operate in adifferent way than is being commanded by control system 214. Thus,operator-initiated alteration of a setting by the operator 260 can causepredictive model generator 210 to relearn a model, predictive mapgenerator 212 to regenerate predictive map 264, control zone generator213 to regenerate the control zones on predictive control zone map 265and control system 214 to relearn its control algorithm or to performmachine learning on one of the controller components 232-246 in controlsystem 214 based upon the adjustment by the operator 260, as shown inblock 322. Block 324 represents the use of other triggered learningcriteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval. This is indicatedby block 326.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 326,then one or more of the predictive model generator 210, predictive mapgenerator 212, control zone generator 213 and control system 214performs machine learning to generate a new predictive model, a newpredictive map, new control zones, and a new control algorithm,respectively, based upon the learning trigger criteria. The newpredictive model, the new predictive map, and the new control algorithmare generated using any additional data that has been collected sincethe last learning operation was performed. Performing relearning isindicated by block 328.

If the harvesting operation has been completed, operation moves fromblock 312 to block 330 where one or more of the predictive map 264,predictive control zone map 265, and predictive model generated bypredictive model generator 210 are stored. The predictive map 264,predictive control zone map 265, and predictive model may be storedlocally on data store 202 or sent to a remote system using communicationsystem 206 for later use.

It will be noted that while some examples herein describe predictivemodel generator 210 and predictive map generator 212 receiving a priorinformation map in generating a predictive model and a functionalpredictive map, respectively, in other examples, the predictive modelgenerator 210 and predictive map generator 212 can receive, ingenerating a predictive model and a functional predictive map,respectively, other types of maps, including predictive maps, such as afunctional predictive map generated during the harvesting operation.

FIG. 4A is a block diagram of a portion of the agricultural harvester100 shown in FIG. 1 . Particularly, FIG. 4A shows, among other things,an example of the predictive map generator 212 in more detail. FIG. 4Aalso illustrates information flow among the various components shown.The predictive model generator 210 receives an information map 259.Information map 259 includes values of an agricultural characteristiccorresponding to different geographic locations in the field. In someexamples, information map 259 can be a prior information map 258. Insome examples, information map 259 can be a predictive map that includespredictive values of an agricultural characteristic corresponding todifferent geographic locations in the field, such as a functionalpredictive map generated using the method described in FIG. 3 .Information map 259 can include, in some examples, one or more oftopographic map 332, seed genotype map 335, VI map 336, yield map 338,biomass map 340, or weed map 342. Predictive model generator 210 alsoreceives a geographic location 334, or an indication of a geographiclocation, from geographic position sensor 204. In-situ sensors 208detect a value of an agricultural characteristic that is indicative of acharacteristic of processed material. Processed material may include, insome examples, grain or other crop, tailings, and MOG. In-situ sensors208 can thus include one or more of tailings characteristic sensor 344that senses a tailings characteristic, loss sensor 346 that senses acharacteristic indicative of grain or crop loss, grain quality sensor348 that senses a characteristic indicative of grain quality, orinternal distribution sensor 350 that senses a characteristic indicativeof internal distribution of processed material in agricultural harvester100, as well as a processing system 352. In some instances, the one ormore sensors 344, 346, 348, and 350 may be located on board theagricultural harvester 100. The processing system 352 processes sensordata generated from the one or more sensors 344, 346, 348, and 350 togenerate processed data 354, some examples of which are described below.

In some examples, one or more sensors 344, 346, 348, and 350 maygenerate electronic signals indicative of the characteristic that thesensor senses. Processing system 352 processes one or more of the sensorsignals obtained via the sensors to generate processed data identifyingone or more characteristics. The characteristics identified by theprocessing system 352 may include an internal material distribution,loss, grain quality, or a tailings characteristic.

In-situ sensor 208 may be or include optical sensors, such as a cameradisposed to view internal portions of agricultural harvester thatprocess the agricultural material. Thus, in some examples, theprocessing system 352 is operable to detect the internal distribution ofthe agricultural material passing through the agricultural harvester 100based on an image captured by internal distribution sensor 350. In otherexamples, the process camera may be clean grain camera 150, andprocessing system 352 is operable to detect grain quality. In otherexamples, the process camera may be configured to capture images oftailings material, and processing system 352 is operable to detecttailings characteristics. In other examples, loss sensor 346 may be orinclude separator loss sensors 148 or loss sensors 152 that sense lossin cleaning system 118 and processing system 352. The loss sensor 346 isoperable to detect crop loss.

In other examples, in-situ sensor 208 may be or includes a GPS sensorthat senses machine position. In this case, processing system 352 canderive speed and direction from the sensor signals as well. In anotherexample, in-situ sensor 208 can include one or more MOG moisture sensorsthat detect moisture characteristics of MOG in one or more subsystems onagricultural harvester 100. Processing system 352, in this case, maydetect and output MOG moisture information.

Other machine properties and sensors may also be used. In some examples,raw or processed data from sensors 344, 346, 348, and 350 may bepresented to operator 260 via operator interface mechanism 218. Operator260 may be onboard the agricultural harvester 100 or at a remotelocation.

FIG. 4B is a block diagram showing one example of predictive modelgenerator 210 in more detail. In the example shown in FIG. 4B,predictive model generator 210 may include one or more of topographiccharacteristic-to-tailings characteristic model generator 356,topographic characteristic-to-grain quality model generator 358,topographic characteristic-to-loss model generator 360, topographiccharacteristic-to-internal distribution model generator 362, vegetativeindex-to-tailings characteristic model generator 364, vegetativeindex-to-grain quality model generator 366, vegetative index-to-lossmodel generator 368, vegetative index-to-internal distribution modelgenerator 370, genotype-to-tailings characteristic model generator 372,genotype-to-grain quality model generator 374, genotype-to-loss modelgenerator 376, genotype-to-internal distribution model generator 378,yield-to-tailings characteristic model generator 380, yield-to-grainquality model generator 382, yield-to-loss model generator 384,yield-to-internal distribution model generator 386, biomass-to-tailingscharacteristic model generator 388, biomass-to-grain quality modelgenerator 390, biomass-to-loss model generator 392, biomass-to-internaldistribution model generator 394, weed characteristic-to-tailingscharacteristic model generator 396, weed characteristic-to-grain qualitymodel generator 398, weed characteristic-to-loss model generator 400,weed characteristic-to-internal distribution model generator 402,combination model generator 404, and other items 406. Each of the modelgenerators shown in FIG. 4B generates a model that models a relationshipbetween values on an information map and values sensed by an in-situsensor 208. Combination model generator 404 may generate one or moremodels based upon data from different combinations of one or moreinformation maps 259 and one or more in-situ sensors 208.

Topographic characteristic-to-tailings characteristic model generator356 receives processed data 354 and a topographic map 332 and models arelationship between the topographic characteristics on topographic map332 and tailings characteristics sensed by tailings characteristicsensor 334. The tailings level can be influenced by the slope ofagricultural harvester 100 both in the fore/aft (tilt) and in theside-to-side (roll) directions. In one example, a machine slope factor,indicating the orientation of agricultural harvester 100, is derivedfrom topographic map 332, although the machine slope factor can beobtained from a machine orientation sensor on agricultural harvester 100as well. There may be different relationships between tailings level andpitch up vs. pitch down vs. roll angles. Thus, in one example,topographic characteristic-to-tailings characteristic model generator356 may generate multiple different models, each modeling a differentrelationship or a single model modeling some or all of therelationships. For example, model generator 356 may generate a modelthat models a relationship between tailings level and pitch up. Modelgenerator 356 may generate a separate model that models a relationshipbetween tailings level and pitch down. In another example, multiplerelationships can be modeled by a single model. Similarly, the longerthat agricultural harvester 100 spends in a given orientation (e.g.,pitch up, pitch down, etc.), the greater the build-up of tailings volumeor tailings level at different locations in agricultural harvester 100.Therefore, model generator 356 may also generate a model that models therelationship between the rate of tailings level increase or decrease andthe slope conditions so that tailings level can be predicted moreaccurately over time. In addition, model generator 356 may generate amodel that considers the chaffer, sieve, and fan speed settings giventhat these settings influence the tailings level and the rate of changeof the tailings level. In addition, the type of material (e.g., thecomposition of the material) in the tailings may be influenced by theslope of agricultural harvester as well. The types of material that maybe identified in the tailings may include clean or free grain,unthreshed grain, and MOG type (e.g., small, large, green, etc.). Thecomposition may include other things as well, such as the type ofmaterial in the tailings or the relative amounts of different materialsin the tailings. Thus, model generator 356 may generate a model thatmodels a relationship between the type of material or composition in thetailings and the slope of agricultural harvester 100.

Topographic characteristic-to-grain quality model generator 358 maygenerate a model that models a relationship between topographiccharacteristics on topographic map 332 and grain quality sensed by grainquality sensor 348. The grain quality in the agricultural harvester 100can be influenced by the slope of agricultural harvester 100 both in thefore/aft and in the side-to-side directions. A slope factor, indicatingthe orientation of agricultural harvester 100, may be derived fromtopographic map 332 or from an orientation sensor on agriculturalharvester 100. There may be different relationships between grainquality characteristics and pitch up vs. pitch down vs. roll angles.Thus, model generator 358 may generate different models that model thesedifferent relationships or a single model that models multiplerelationships. Similarly, different grain quality characteristics may bemodeled by separate models or may be part of a cumulative model. Suchgrain quality characteristics may include clean grain, broken grain,unthreshed grain, MOG levels, and MOG types. Also, the longer thatagricultural harvester 100 spends in a particular orientation, the morethe grain quality characteristics may be affected. Therefore, modelgenerator 358 may generate a model that models a relationship betweenthe rate of change of the grain quality characteristic and slopeconditions so that the grain quality characteristics can be moreaccurately predicted over longer periods of time. Also, the chaffer,sieve, fan speed, separator speed, thresher speed, and concave clearancesettings can influence the grain quality characteristics and the changesand rates of change in the grain quality characteristics. Thus, modelgenerator 358 can generate separate models modeling the relationshipsbetween one or more of the chaffer, sieve, fan speed, separator speed,thresher speed, and concave clearance and the grain qualitycharacteristics and the rates of change in the grain qualitycharacteristics, or model generator 358 can generate cumulative modelsthat model more than one of those relationships.

Grain loss from agricultural harvester 100 may be heavily influenced bythe slope on which the agricultural harvester 100 is operating. Thus,grain loss may be heavily influenced by the orientation of agriculturalharvester 100. The pitch of agricultural harvester 100 influences thedwell time of the grain on the cleaning subsystem 118 and may influencehow effectively the grain can be separated. The side slope (or rollorientation) of agricultural harvester 100 may determine how much grainpiles up or overloads one side of the cleaning subsystem 118,essentially underutilizing the other side of the cleaning subsystem 118and resulting in losses on one side of agricultural harvester 100 due tomaterial piling up on one side of the cleaning subsystem 118. While thisphenomenon is repeatable, grain levels inside of agricultural harvester100 are variable depending on slope severity, the amount of time thatagricultural harvester 100 spends on a slope, machine settings, and cropconditions. Thus, topographic characteristic-to-loss model generator 360models a relationship between a topographic characteristic from map 332and the output of loss sensor 346.

Similarly, topographic characteristic-to-internal distribution modelgenerator 362 may generate a model that models a relationship betweenthe topographic characteristic on map 332 (such as the slope, which maydetermine the orientation of agricultural harvester 100) and theinternal distribution of material within agricultural harvester 100. Theinternal distribution may affect loss and other items withinagricultural harvester 100. Also, the amount of time on the slope caninfluence both the loss, the rate of change of loss, the materialdistribution, and the rate of change of the material distribution.Therefore, topographic characteristic-to-loss model generator 360 canmodel the relationships between the amount of time that agriculturalharvester 100 is in a given orientation and loss. Topographiccharacteristic-to-internal distribution model 362 can model arelationship between the amount of time that agricultural harvester 100is in a given orientation and internal material distribution withinagricultural harvester 100.

Vegetative index-to-tailings characteristic model generator 364 maygenerate a model that models a relationship between characteristics onVI map 336 and tailings characteristics sensed by tailingscharacteristic sensor 334. Model generator 364 may also receive an inputfrom other sensors, such as a grain moisture sensor or a MOG moisturesensor. The amount of crop material being processed by agriculturalharvester 100 may be estimated or indicated by the characteristics on VImap 336. This may also impact the tailings characteristics such as thetailings composition, the tailings levels, the tailings flow, or thetailings volume in agricultural harvester 100. Thus, vegetativeindex-to-tailings characteristic model generator 364 models arelationship between the vegetative index characteristics on VI map 336and the outputs from tailings characteristic sensor 344.

Vegetative index-to-grain quality model generator 366 may generate amodel that models a relationship between the VI characteristics on VImap 336 and the output of grain quality sensor 348. The grain qualitycharacteristics sensed by grain quality sensor 348 may include, asdiscussed above, clean grain, broken grain, unthreshed grain, MOGlevels, and MOG types entering the clean grain tank. It may bedifficult, for example, when harvesting canola, to generate a fan speedthat retains all of the grain but blows out all of the pods, pieces ofstem and pith, etc. The effectiveness of this fan in doing this may bedependent upon the biomass of the plant material, which, itself, may bedependent on the moisture content of the plant which can be indicated byVI characteristics. Thus, the relationship between the characteristicson VI map 336 and the grain quality characteristics sensed by sensor 348can be used by model generator 366 to generate a model that models thatrelationship.

In addition, when more biomass or more grain comes through agriculturalharvester 100 at a particular time, this can lead to higher loss values.Similarly, the amplitude and frequency of biomass variance can lead toloss values as well. For example, a short duration of higher crop volumeor density may have a short impact, but if the higher crop volume ordensity repeats frequently, this can lead to higher loss values. Thus,vegetative index-to-loss model generator 368 may generate a model thatmodels the relationship between the values on VI map 336 and the outputof loss sensors 346.

As discussed above, the amount of biomass being processed byagricultural harvester 100 may also affect the internal distribution ofmaterial within agricultural harvester 100. Higher levels of biomass maylead to higher levels of material in different areas of agriculturalharvester 100. Therefore, model generator 370 can generate a model thatmodels a relationship between the characteristics on VI map 336 and theoutput of internal distribution sensor 350.

Different plant genotypes have different characteristics that canmanifest themselves in how well grain is separated from the MOG or howrobust parts of the plant (such as corn cobs or grain) are. Thesecharacteristics can affect the tailings characteristics, such as thecomposition of unthreshed grain in the tailings, the amount of MOG inthe tailings, whether the MOG is broken into larger or smaller pieces,etc. Therefore, genotype-to-tailings characteristic model generator 372can generate a relationship between genotype values on seed genotype map335 and tailings characteristic sensor values generated by tailingscharacteristic sensor 334.

Similarly, different genotypes may perform differently with respect tograin quality. For example, under a given set of machine settings onagricultural harvester 100, different genotypes may result in differentamounts of broken grain, unthreshed grain, MOG levels, and MOG types.Genotype-to-grain quality model generator 374 thus generates a modelthat models a relationship between the seed genotype characteristics onseed genotype map 335 and the grain quality characteristics sensed bygrain quality sensor 348.

The size or mass of grain can also differ by genotype. This may resultin different loss levels in that larger grain may have a higher tendencyto bounce out of agricultural harvester 100 while smaller grain may havea larger tendency to be blown out by the cleaning fan. Differentgenotypes may also have different plant compositions and thus impactloss levels due to how the crop breaks down during processing withinagricultural harvester 100. Thus, genotype-to-loss model generator 376may generate a model that models a relationship between the seedgenotype characteristics on map 335 and the output of loss sensor 346.

Different genotypes may also lead to different internal distributions.For example, crops with different relative maturities may have, at thetime of harvest, different MOG moisture levels, which can lead to moreor less material being processed by agricultural harvester 100 at anygiven time. Thus, genotype-to-internal distribution model generator 378may generate a model that models a relationship between the seedgenotype characteristics on seed genotype map 335 and the outputs ofinternal distribution sensor 350. map 335 and the outputs of internaldistribution sensor 350.

Yield may also affect the tailings characteristics. Higher yield areasin a field may generate more tailings with a different composition thanlower yield areas. Thus, yield-to-tailings characteristic modelgenerator 380 may generate a model that models a relationship betweenthe predictive yield values on yield map 338 and the outputs fromtailings characteristic sensor 334.

The yield may also affect the grain quality. For instance, in areas ofincreased yield, separating the MOG from the grain may be moredifficult, resulting in more MOG in the clean grain tank 132 of theagricultural harvester 100. Therefore, yield-to-grain quality modelgenerator 382 may generate a model that models a relationship betweenthe predictive yield values on yield map 338 and the grain qualitycharacteristics sensed by grain quality sensor 348.

Yield may also affect loss. When more grain is coming throughagricultural harvester 100, higher loss levels may result. Thus, higheryield areas may produce higher loss levels as well. Yield-to-loss modelgenerator 384 may thus generate a model that models a relationshipbetween predictive yield values on yield map 338 and the loss valuesoutput by loss sensor 346.

Yield can also have an effect on the internal distribution of materialwithin agricultural harvester 100. Higher yield areas are oftenaccompanied by larger biomass levels being processed by agriculturalharvester 100. The larger biomass levels that often accompany higheryield areas can affect the amount and distribution of material withinagricultural harvester 100. Therefore, yield-to-internal distributionmodel generator 386 may generate a model that models a relationshipbetween the predictive yield values on yield map 338 and the internaldistribution characteristics sensed by internal distribution sensor 350.

The amount of biomass being processed by agricultural harvester 100 mayalso affect the tailings characteristics. When more biomass is beingprocessed by agricultural harvester 100 at a given time, larger tailingsvolumes may result, and the composition of the tailings may also beaffected. In areas of heavy crop and, thus, increased biomass levels,the likelihood that more unthreshed grain will present in the tailingsmay be increased if machine settings on agricultural harvester 100 arenot adjusted to account for the increased biomass. In areas of lightcrop and, thus, decreased biomass levels, an increase in chaff load,and, thus, an increase in tailings, may result unless machine settingson agricultural harvester 100 are adjusted to account for the reducedbiomass. Therefore, biomass-to-tailings characteristic model generator388 may generate a model that models a relationship between biomasscharacteristic values on biomass map 340 and tailings characteristicssensed by tailings characteristic sensor 344.

Biomass may also affect grain quality. Higher biomass levels may affectthreshing and cleaning, meaning that there may be more unthreshed grain.As a result, more grain that is not cleaned adequately may be enteringthe clean grain tank. Thus, biomass-to-grain quality model generator 390may generate a model that models a relationship between biomasscharacteristics on biomass map 340 and grain quality characteristicssensed by grain quality sensor 348.

Biomass may also be related to grain loss. For instance, higher biomasslevels often mean increased MOG in the agricultural harvester 100 whichcan lead to increased grain loss Therefore, biomass-to-loss modelgenerator 392 may generate a model that models a relationship betweenthe biomass characteristics on biomass map 340 and the losscharacteristics sensed by loss sensor 346.

Biomass levels may also be related to the internal distribution ofmaterial within agricultural harvester 100. For instance, variation inbiomass levels being processed by harvester 100 may lead to unevenlevels of material distribution in agricultural harvester 100, such thatmaterial levels can be variable per location within the agriculturalharvester, such as an increase of material in one location and adecrease of material in another location as a result of changes in thebiomass being processed by the agricultural harvester 100. Therefore,biomass-to-internal distribution model generator 394 may generate amodel that models a relationship between biomass characteristics onbiomass map 340 and internal distribution characteristics sensed byinternal distribution characteristics sensed by internal distributionsensor 350.

Tailings characteristics may be strongly influenced by weedcharacteristics, such as the amount of weeds (e.g., weed intensity) thatare taken into agricultural harvester 100. The weed material istypically tougher and greener than the crop material and, thus, has agreater likelihood of reaching the tailings system, which can cause hightailings volumes and plugs in agricultural harvester 100. Therefore,weed characteristic-to-tailings characteristic model generator 396 maygenerate a model that models a relationship between weed characteristicson weed map 342 and tailings characteristics sensed by tailingscharacteristic sensor 344. In addition, when agricultural harvester 100spends longer periods of time in an area that has a relatively high weedintensity, relative to other areas of the field, this can lead toincreasing levels of tailings. Therefore, model generator 396 maygenerate a model that models a relationship between the rate of changein tailings and a size of a location in the field that has a relativelyhigh weed intensity.

Weed characteristics, such as weed intensity or weed type, may also berelated to grain quality. For instance, higher weed intensity levels maylead to heavier MOG levels in the cleaning shoe, which increases theamounts of MOG that are delivered to the clean grain tank. Therefore,weed characteristic-to-grain quality model generator 398 may generate amodel that models a relationship between weed characteristics on weedmap 342 and grain quality characteristics sensed by grain quality sensor348.

Weed characteristics, such as weed intensity and weed type, may also berelated to loss. For instance, a higher weed intensity may result inheavier MOG levels which may increase grain loss. Therefore, weedcharacteristic-to-loss model generator 400 may generate a model thatmodels a relationship between the weed characteristics on weed map 342and the loss characteristics sensed by loss sensor 346.

Weed characteristics, such as weed intensity or weed type, may also berelated to the internal distribution of material within agriculturalharvester 100. Therefore, weed characteristic-to-internal distributionmodel generator 402 may generate a model that models a relationshipbetween the weed characteristic values on weed map 342 and the internaldistribution characteristics sensed by internal distribution sensor 350.

Returning again to FIG. 4A, predictive map generator 212 may include oneor more of tailings characteristic map generator 410, loss map generator412, grain quality map generator 414, and internal distribution mapgenerator 416. A number of examples of different combinations of in-situsensors 208 and information maps 259 will now be described.

The present discussion proceeds with respect to an example in whichin-situ sensor 208 is an internal distribution sensor 350 that sensesinternal material distribution in agricultural harvester 100. It will beappreciated that this is just one example, and the sensors mentionedabove, as other examples of in-situ sensor 208, are contemplated herein,as are other information maps 259, as well. Predictive model generator210 (shown in more detail in FIG. 4B) identifies a relationship betweenmaterial distribution detected in processed data 354 (e.g., the materialdistribution in agricultural harvester 100 can be identified based onsensor signals from internal distribution sensor 350), at a geographiclocation corresponding to where the sensor data was derived from, andcharacteristics from one or more of the information maps 259corresponding to the same location in the field where the materialdistribution was detected. Based on this relationship established bypredictive model generator 210, predictive model generator 210 generatesa predictive model 408. The predictive model 408 is used by internaldistribution map generator 416 to predict material distribution withinagricultural harvester 100 at different locations in the field basedupon the georeferenced topographic characteristic contained in theinformation map 259 at the same locations in the field.

The present discussion proceeds with respect to an example in whichmachine sensor 208 is a grain loss sensor 346. It will be appreciatedthat this is just one example, and the sensors mentioned above, as otherexamples of in-situ sensor 208, as well as the other information maps259 are contemplated herein as well. Predictive model generator 210(shown in more detail in FIG. 4B) identifies a relationship betweengrain loss detected in processed data 354 at a geographic locationcorresponding to where the sensor data was geolocated, andcharacteristics from the information map 259 corresponding to the samelocation in the field where the grain loss was geolocated. Based on thisrelationship established by predictive model generator 210, predictivemodel generator 210 generates a predictive model 408. The predictivemodel 408 is used by loss map generator 412 to predict grain loss atdifferent locations in the field based upon the georeferencedcharacteristic contained in the information map 259 at the samelocations in the field.

The present discussion proceeds with respect to an example in whichin-situ sensor 208 is a tailings characteristic sensor 344. It will beappreciated that this is just one example, and the sensors mentionedabove, as other examples of in-situ sensor 336, as well as the otherinformation maps 259 are contemplated herein as well. Predictive modelgenerator 210 (shown in more detail in FIG. 4B) identifies arelationship between tailings characteristic detected in processed data354 at a geographic location corresponding to where the sensor data wasgeolocated and characteristics from the information map 259corresponding to the same location in the field where the tailingscharacteristic was geolocated. Based on this relationship established bypredictive model generator 210, predictive model generator 210 generatesa predictive model 408. The predictive model 408 is used by tailingscharacteristic map generator 410 to predict tailing characteristics atdifferent locations in the field based upon the georeferencedcharacteristic contained in the information map 259 at the samelocations in the field.

The present discussion proceeds with respect to an example in whichin-situ sensor 208 is a grain quality sensor 348. It will be appreciatedthat this is just one example, and the sensors mentioned above, as otherexamples of in-situ sensor 208, as well as the other information maps259 are contemplated herein as well. Predictive model generator 210(shown in more detail in FIG. 4B) identifies a relationship betweengrain quality detected in processed data 354 at a geographic locationcorresponding to where the sensor data was geolocated, andcharacteristics from the information map 259 corresponding to the samelocation in the field where the grain quality was geolocated. Based onthis relationship established by predictive model generator 210,predictive model generator 210 generates a predictive model 408. Thepredictive model 408 is used by grain quality map generator 414 topredict grain quality at different locations in the field based upon thegeoreferenced characteristic contained in the information map 259 at thesame locations in the field.

The predictive model generator 210 is operable to produce a plurality ofpredictive models, such as one or more of the predictive modelsgenerated by the model generators shown in FIG. 4B. In another example,two or more of the predictive models described above may be combinedinto a single predictive model that predicts two or more characteristicsof, for instance, internal material distribution, tailingscharacteristic, loss, and grain quality based upon the characteristicsfrom one or more of the information maps 259 at different locations inthe field. Any of these machine models, or combinations thereof, arerepresented collectively by machine model 408 in FIG. 4A.

The predictive machine model 408 is provided to predictive map generator212. In the example of FIG. 4A, predictive map generator 212 includes aninternal distribution map generator 416, a loss map generator 412, atailings characteristic map generator 410, and a grain quality mapgenerator 414. In other examples, the predictive map generator 212 mayinclude additional, fewer, or different map generators. Thus, in someexamples, the predictive map generator 212 may include other items 417which may include other types of map generators to generate maps forother types of characteristics.

Tailings characteristic map generator 410 illustratively generates apredictive tailings map 418 that predicts tailing characteristics atdifferent locations in the field based upon the characteristics in theinformation map 259 at those locations in the field and the predictivemodel 408.

Loss map generator 412 illustratively generates a predictive loss map420 that predicts grain loss at different locations in the field basedupon the characteristics in the information map 259 at those locationsin the field and the predictive model 408.

Grain quality map generator 414 illustratively generates a predictivegrain quality map 422 that predicts a characteristic indicative of grainquality at different locations in the field based upon thecharacteristics in the information map 259 at those locations in thefield and the predictive model 408.

Internal distribution map generator 416 illustratively generates apredictive internal distribution map 424 that predicts materialdistribution at different locations in the field based upon thecharacteristics in the information map 259 at those locations in thefield and the predictive model 408.

Predictive map generator 212 outputs one or more of the functionalpredictive maps 418, 420, 422, and 424 that are predictive of acharacteristic. Each of the functional predictive maps 418, 420, 422,and 424 are functional predictive maps that predict the respectivecharacteristic at different locations in a field. Each of the functionalpredictive maps 418, 420, 422, and 444 may be provided to control zonegenerator 213, control system 214, or both. Control zone generator 213generates control zones and incorporates those control zones into thefunctional predictive maps 418, 420, 422, and 424. Any or all of thefunctional predictive maps 418, 420, 422, or 424 and the correspondingfunctional predictive maps 418, 420, 422, or 424 with control zones maybe provided to control system 214, which generates control signals tocontrol one or more of the controllable subsystems 216 based upon one orall of the functional predictive maps. Any or all of the functionalpredictive maps 418, 420, 422, or 424 (with or without control zones)may be presented to operator 260 or another user.

FIG. 5 is a flow diagram of an example of operation of predictive modelgenerator 210 and predictive map generator 212 in generating thepredictive machine model 408 and the predictive characteristic maps 418,420, 422 and 424, respectively. At block 430, predictive model generator210 and predictive map generator 212 receive an information map 259,which can be one or more of the information maps shown in FIG. 4A. Atblock 432, processing system 352 receives one or more sensor signalsfrom in-situ sensors 208. As discussed above, the in-situ sensor 208 maybe a tailings characteristic sensor 344, loss sensor 346, a grainquality sensor 348, or an internal distribution sensor 350.

At block 434, processing system 352 processes the one or more receivedsensor signals to generate data indicative of a characteristic. In someinstances, as indicated at block 436, the sensor data may be indicativeof a tailings characteristic. In some instances, as indicated at block438, the sensor data may be indicative of grain loss. In some instances,as indicated by block 440, the sensor data may be indicative of grainquality. In some instances, as indicated at block 442, the sensor datamay be indicative of internal material distribution within agriculturalharvester 100.

At block 444, predictive model generator 210 also obtains the geographiclocation 334 corresponding to the sensor data. For instance, thepredictive model generator 210 can obtain the geographic position fromgeographic position sensor 204 and determine, based upon machine delays,machine speed, etc., a precise geographic location where the sensor datawas captured or derived. Additionally, at block 444, the orientation ofthe agricultural harvester 100 on the field may be determined. Theorientation of agricultural harvester 100 may be obtained, for instance,to identify its orientation relative to the slope on the field.

At block 446, predictive model generator 210 generates one or morepredictive models, such as machine model 408, that model a relationshipbetween one or more characteristics on an information map 259 and acharacteristic being sensed by the in-situ sensor 208 or a relatedcharacteristic.

At block 448, the predictive model, such as predictive model 408, isprovided to predictive map generator 212, and the predictive mapgenerator 212 generates a functional predictive map that maps apredicted characteristic based on the georeferenced data in aninformation map 259 and the predictive model 408. In some examples, thefunctional predictive map is predictive tailings characteristic map 418.In some examples, the functional predictive map is predictive loss map420. In some examples, the functional predictive map is predictive grainquality map 422. In some examples, the functional predictive map ispredictive internal distribution map 424.

The functional predictive map can be generated during the course of anagricultural operation. Thus, as an agricultural harvester is movingthrough a field performing an agricultural operation, the functionalpredictive map is generated as the agricultural operation is beingperformed.

At block 450, predictive map generator 212 outputs the functionalpredictive map. At block 452, predictive map generator 212 outputs thefunctional predictive map for presentation and possible interaction byoperator 260. At block 454, predictive map generator 212 may configurethe functional predictive map for consumption by control system 214. Atblock 456, predictive map generator 212 can also provide the functionalpredictive map to control zone generator 213 for generation of controlzones. At block 428, predictive map generator 212 configures thefunctional predictive map in other ways as well. The functionalpredictive map (with or without the control zones) is provided tocontrol system 214. At block 460, control system 214 generates controlsignals to control the controllable subsystems 216 based upon thefunctional predictive map.

The control system 214 may generate control signals to control actuatorsthat control one or more of the speed and size of openings in sieve 124and chaffer 122, the speed of cleaning fan 120 and rotor 112, the rotorpressure driving rotor 112, and the clearance between rotor 112 andconcaves 114, or other things.

Control system 214 can generate control signals to control header orother machine actuator(s) 248. Control system 214 can generate controlsignals to control propulsion subsystem 250. Control system 214 cangenerate control signals to control steering subsystem 252. Controlsystem 214 can generate control signals to control residue subsystem138. Control system 214 can generate control signals to control machinecleaning subsystem 254. Control system 214 can generate control signalsto control thresher 110. Control system 214 can generate control signalsto control material handling subsystem 125. Control system 214 cangenerate control signals to control crop cleaning subsystem 118. Controlsystem 214 can generate control signals to control communication system206. Control system 214 can generate control signals to control operatorinterface mechanisms 218. Control system 214 can generate controlsignals to control various other controllable subsystems 256.

In an example in which control system 214 receives a functionalpredictive map or a functional predictive map with control zones added,header/reel controller 238 controls header or other machine actuators248 to control a height, tilt, or roll of header 102. In an example inwhich control system 214 receives a functional predictive map or afunctional predictive map with control zones added, feed rate controller236 controls propulsion subsystem 250 to control a travel speed ofagricultural harvester 100. In an example in which control system 214receives a functional predictive map or a functional predictive map withcontrol zones added, the path planning controller 234 controls steeringsubsystem 252 to steer agricultural harvester 100. In another example inwhich control system 214 receives a functional predictive map or afunctional predictive map with control zones added, the residue systemcontroller 244 controls residue subsystem 138. In another example inwhich control system 214 receives a functional predictive map or afunctional predictive map with control zones added, the settingscontroller 232 controls thresher settings of thresher 110. In anotherexample in which control system 214 receives a functional predictive mapor a functional predictive map with control zones added, the settingscontroller 232 or another controller 246 controls material handlingsubsystem 125. In another example in which control system 214 receives afunctional predictive map or a functional predictive map with controlzones added, the settings controller 232 controls crop cleaningsubsystem 118. In another example in which control system 214 receives afunctional predictive map or a functional predictive map with controlzones added, the machine cleaning controller 245 controls machinecleaning subsystem 254 on agricultural harvester 100. In another examplein which control system 214 receives a functional predictive map or afunctional predictive map with control zones added, the communicationsystem controller 229 controls communication system 206. In anotherexample in which control system 214 receives a functional predictive mapor a functional predictive map with control zones added, the operatorinterface controller 231 controls operator interface mechanisms 218 onagricultural harvester 100. In another example in which control system214 receives the functional predictive map or the functional predictivemap with control zones added, the deck plate position controller 242controls machine/header actuators 248 to control a deck plate onagricultural harvester 100. In another example in which control system214 receives the functional predictive map or the functional predictivemap with control zones added, the draper belt controller 240 controlsmachine/header actuators 248 to control a draper belt on agriculturalharvester 100. In another example in which control system 214 receivesthe functional predictive map or the functional predictive map withcontrol zones added, the other controllers 246 control othercontrollable subsystems 256 on agricultural harvester 100.

In some examples, control system 214 receives a functional predictivemap or a functional predictive map with control zones added andgenerates control signals to one or more of the controllable subsystems216 to control or compensate for the internal material distributionwithin agricultural harvester 100. For instance, control system 214 cangenerate one or more control signals to control material handlingsubsystem 125 to control or compensate for the internal materialdistribution within agricultural harvester 100 based on the receivedfunctional predictive map (with or without control zones). For example,control system 214 can generate one or more control signals to controlthe settings or operating characteristics of components of materialhandling subsystem 125 such as controlling feed accelerator 108,controlling thresher 110, such as controlling the speed of threshingrotor 112, the concave clearance (spacing between threshing rotor 112and concaves 114), controlling separator 116, controlling dischargebeater 126, controlling tailings elevator 128, controlling clean grainelevator 130, controlling unloading auger 134, or controlling spout 136,based on the values in the functional predictive map (with or withoutcontrol zones). In another example, control system 214 can generate oneor more control signals to control cleaning subsystem 118 to control orcompensate for the internal material distribution within agriculturalharvester based on the received functional predictive map (with orwithout control zones). For example, control system 214 can generate oneor more control signals to control the settings or operatingcharacteristics of components of cleaning subsystem 118 such ascontrolling cleaning fan 120, such as increasing or decreasing the speedof cleaning fan 120, controlling chaffer 122, such as controlling thechaffer clearance (controlling the size of the openings in chaffer 122),or controlling sieve 124, such as controlling the sieve clearance(controlling the size of the openings in sieve 124, based on the valuesin the functional predictive map (with or without control zones). Inanother example, control system 214 can generate one or more controlsignals to control residue subsystem 138 to control or compensate forthe internal material distribution within agricultural harvester 100based on the received functional predictive map (with or without controlzones). For example, control system 214 can generate one or more controlsignals to control the settings or operating characteristics ofcomponents of residue subsystem 138 such as controlling chopper 140 orcontrolling spreader 142 based on the values in the functionalpredictive map (with or without control zones).

In some examples, control system 214 can generate one or more controlsignals to control the settings (e.g., position, orientation, etc.) ofthe adjustable material engaging elements disposed within the materialflow path within agricultural harvester to control or compensate for theinternal material distribution within agricultural harvester 100. Forexample, the one or more control signals can control an actuator toactuate movement of the adjustable material engaging elements to changea position or orientation of the adjustable material engaging elementsto direct at least a portion of the material stream right or leftrelative to the direction of flow. In some examples, the direction maybe from areas of greater material depth to areas of less material depthlaterally or fore and aft relative to the direction of material flow.

It can thus be seen that the present system takes one or moreinformation maps that map characteristics to different locations in afield. The present system also uses one or more in-situ sensors thatsense in-situ sensor data that is indicative of a characteristic, andgenerates a model that models a relationship between the characteristicsensed using the in-situ sensor, or a related characteristic, and thecharacteristic mapped in the information map. Thus, the present systemgenerates a functional predictive map using a model, in-situ data, andan information map and may configure the generated functional predictivemap for consumption by a control system or for presentation to a localor remote operator or other user. For example, the control system mayuse the map to control one or more systems of an agricultural harvester.

FIG. 6 shows a block diagram illustrating one example of control zonegenerator 213. Control zone generator 213 includes work machine actuator(WMA) selector 486, control zone generation system 488, and regime zonegeneration system 490. Control zone generator 213 may also include otheritems 492. Control zone generation system 488 includes control zonecriteria identifier component 494, control zone boundary definitioncomponent 496, target setting identifier component 498, and other items520. Regime zone generation system 490 includes regime zone criteriaidentification component 522, regime zone boundary definition component524, settings resolver identifier component 526, and other items 528.Before describing the overall operation of control zone generator 213 inmore detail, a brief description of some of the items in control zonegenerator 213 and the respective operations thereof will first beprovided.

Agricultural harvester 100, or other work machines, may have a widevariety of different types of controllable actuators that performdifferent functions. The controllable actuators on agriculturalharvester 100 or other work machines are collectively referred to aswork machine actuators (WMAs). Each WMA may be independentlycontrollable based upon values on a functional predictive map, or theWMAs may be controlled as sets based upon one or more values on afunctional predictive map. Therefore, control zone generator 213 maygenerate control zones corresponding to each individually controllableWMA or corresponding to the sets of WMAs that are controlled incoordination with one another.

WMA selector 486 selects a WMA or a set of WMAs for which correspondingcontrol zones are to be generated. Control zone generation system 488then generates the control zones for the selected WMA or set of WMAs.For each WMA or set of WMAs, different criteria may be used inidentifying control zones. For example, for one WMA, the WMA responsetime may be used as the criteria for defining the boundaries of thecontrol zones. In another example, wear characteristics (e.g., how mucha particular actuator or mechanism wears as a result of movementthereof) may be used as the criteria for identifying the boundaries ofcontrol zones. Control zone criteria identifier component 494 identifiesparticular criteria that are to be used in defining control zones forthe selected WMA or set of WMAs. Control zone boundary definitioncomponent 496 processes the values on a functional predictive map underanalysis to define the boundaries of the control zones on thatfunctional predictive map based upon the values in the functionalpredictive map under analysis and based upon the control zone criteriafor the selected WMA or set of WMAs.

Target setting identifier component 498 sets a value of the targetsetting that will be used to control the WMA or set of WMAs in differentcontrol zones. For instance, if the selected WMA is propulsion system250 and the functional predictive map under analysis is a functionalpredictive speed map 438, then the target setting in each control zonemay be a target speed setting based on speed values contained in thefunctional predictive speed map 238 within the identified control zone.

In some examples, where agricultural harvester 100 is to be controlledbased on a current or future location of the agricultural harvester 100,multiple target settings may be possible for a WMA at a given location.In that case, the target settings may have different values and may becompeting. Thus, the target settings need to be resolved so that only asingle target setting is used to control the WMA. For example, where theWMA is an actuator in propulsion system 250 that is being controlled inorder to control the speed of agricultural harvester 100, multipledifferent competing sets of criteria may exist that are considered bycontrol zone generation system 488 in identifying the control zones andthe target settings for the selected WMA in the control zones. Forinstance, different target settings for controlling machine speed may begenerated based upon, for example, a detected or predicted feed ratevalue, a detected or predictive fuel efficiency value, a detected orpredicted grain loss value, or a combination of these. However, at anygiven time, the agricultural harvester 100 cannot travel over the groundat multiple speeds simultaneously. Rather, at any given time, theagricultural harvester 100 travels at a single speed. Thus, one of thecompeting target settings is selected to control the speed ofagricultural harvester 100.

Therefore, in some examples, regime zone generation system 490 generatesregime zones to resolve multiple different competing target settings.Regime zone criteria identification component 522 identifies thecriteria that are used to establish regime zones for the selected WMA orset of WMAs on the functional predictive map under analysis. Somecriteria that can be used to identify or define regime zones include,for example, crop type or crop variety based on an as-planted map oranother source of the crop type or crop variety, weed type, weedintensity, crop state, such as whether the crop is down, partially downor standing, yield, biomass, vegetative index, or topography. Just aseach WMA or set of WMAs may have a corresponding control zone, differentWMAs or sets of WMAs may have a corresponding regime zone. Regime zoneboundary definition component 524 identifies the boundaries of regimezones on the functional predictive map under analysis based on theregime zone criteria identified by regime zone criteria identificationcomponent 522.

In some examples, regime zones may overlap with one another. Forinstance, a crop variety regime zone may overlap with a portion of or anentirety of a crop state regime zone. In such an example, the differentregime zones may be assigned to a precedence hierarchy so that, wheretwo or more regime zones overlap, the regime zone assigned with agreater hierarchical position or importance in the precedence hierarchyhas precedence over the regime zones that have lesser hierarchicalpositions or importance in the precedence hierarchy. The precedencehierarchy of the regime zones may be manually set or may beautomatically set using a rules-based system, a model-based system, oranother system. As one example, where a downed crop regime zone overlapswith a crop variety regime zone, the downed crop regime zone may beassigned a greater importance in the precedence hierarchy than the cropvariety regime zone so that the downed crop regime zone takesprecedence.

In addition, each regime zone may have a unique settings resolver for agiven WMA or set of WMAs. Settings resolver identifier component 526identifies a particular settings resolver for each regime zoneidentified on the functional predictive map under analysis and aparticular settings resolver for the selected WMA or set of WMAs.

Once the settings resolver for a particular regime zone is identified,that settings resolver may be used to resolve competing target settings,where more than one target setting is identified based upon the controlzones. The different types of settings resolvers can have differentforms. For instance, the settings resolvers that are identified for eachregime zone may include a human choice resolver in which the competingtarget settings are presented to an operator or other user forresolution. In another example, the settings resolver may include aneural network or other artificial intelligence or machine learningsystem. In such instances, the settings resolvers may resolve thecompeting target settings based upon a predicted or historic qualitymetric corresponding to each of the different target settings. As anexample, an increased vehicle speed setting may reduce the time toharvest a field and reduce corresponding time-based labor and equipmentcosts but may increase grain losses. A reduced vehicle speed setting mayincrease the time to harvest a field and increase correspondingtime-based labor and equipment costs but may decrease grain losses. Whengrain loss or time to harvest is selected as a quality metric, thepredicted or historic value for the selected quality metric, given thetwo competing vehicle speed settings values, may be used to resolve thespeed setting. In some instances, the settings resolvers may be a set ofthreshold rules that may be used instead of, or in addition to, theregime zones. An example of a threshold rule may be expressed asfollows:

-   -   If predicted biomass values within 20 feet of the header of the        agricultural harvester 100 are greater that x kilograms (where x        is a selected or predetermined value), then use the target        setting value that is chosen based on feed rate over other        competing target settings, otherwise use the target setting        value based on grain loss over other competing target setting        values.

The settings resolvers may be logical components that execute logicalrules in identifying a target setting. For instance, the settingsresolver may resolve target settings while attempting to minimizeharvest time or minimize the total harvest cost or maximize harvestedgrain or based on other variables that are computed as a function of thedifferent candidate target settings. A harvest time may be minimizedwhen an amount to complete a harvest is reduced to at or below aselected threshold. A total harvest cost may be minimized where thetotal harvest cost is reduced to at or below a selected threshold.Harvested grain may be maximized where the amount of harvested grain isincreased to at or above a selected threshold.

FIG. 7 is a flow diagram illustrating one example of the operation ofcontrol zone generator 213 in generating control zones and regime zonesfor a map that the control zone generator 213 receives for zoneprocessing (e.g., for a map under analysis).

At block 530, control zone generator 213 receives a map under analysisfor processing. In one example, as shown at block 532, the map underanalysis is a functional predictive map. For example, the map underanalysis may be one of the functional predictive maps 436, 437, 438, or440. Block 534 indicates that the map under analysis can be other mapsas well.

At block 536, WMA selector 486 selects a WMA or a set of WMAs for whichcontrol zones are to be generated on the map under analysis. At block538, control zone criteria identification component 494 obtains controlzone definition criteria for the selected WMAs or set of WMAs. Block 540indicates an example in which the control zone criteria are or includewear characteristics of the selected WMA or set of WMAs. Block 542indicates an example in which the control zone definition criteria areor include a magnitude and variation of input source data, such as themagnitude and variation of the values on the map under analysis or themagnitude and variation of inputs from various in-situ sensors 208.Block 544 indicates an example in which the control zone definitioncriteria are or include physical machine characteristics, such as thephysical dimensions of the machine, a speed at which differentsubsystems operate, or other physical machine characteristics. Block 546indicates an example in which the control zone definition criteria areor include a responsiveness of the selected WMA or set of WMAs inreaching newly commanded setting values. Block 548 indicates an examplein which the control zone definition criteria are or include machineperformance metrics. Block 550 indicates an example in which the controlzone definition criteria are or includes operator preferences. Block 552indicates an example in which the control zone definition criteria areor include other items as well. Block 549 indicates an example in whichthe control zone definition criteria are time based, meaning thatagricultural harvester 100 will not cross the boundary of a control zoneuntil a selected amount of time has elapsed since agricultural harvester100 entered a particular control zone. In some instances, the selectedamount of time may be a minimum amount of time. Thus, in some instances,the control zone definition criteria may prevent the agriculturalharvester 100 from crossing a boundary of a control zone until at leastthe selected amount of time has elapsed. Block 551 indicates an examplein which the control zone definition criteria are based on a selectedsize value. For example, a control zone definition criteria that isbased on a selected size value may preclude definition of a control zonethat is smaller than the selected size. In some instances, the selectedsize may be a minimum size.

At block 554, regime zone criteria identification component 522 obtainsregime zone definition criteria for the selected WMA or set of WMAs.Block 556 indicates an example in which the regime zone definitioncriteria are based on a manual input from operator 260 or another user.Block 558 illustrates an example in which the regime zone definitioncriteria are based on crop type or crop variety. Block 560 illustratesan example in which the regime zone definition criteria are based onweed characteristics, such as weed type or weed intensity or both. Block561 illustrates an example in which the regime zone definition criteriaare based on or include topography. Block 562 illustrates an example inwhich the regime zone definition criteria are based on or include cropstate. Block 564 indicates an example in which the regime zonedefinition criteria are or include other criteria as well.

At block 566, control zone boundary definition component 496 generatesthe boundaries of control zones on the map under analysis based upon thecontrol zone criteria. Regime zone boundary definition component 524generates the boundaries of regime zones on the map under analysis basedupon the regime zone criteria. Block 568 indicates an example in whichthe zone boundaries are identified for the control zones and the regimezones. Block 570 shows that target setting identifier component 498identifies the target settings for each of the control zones. Thecontrol zones and regime zones can be generated in other ways as well,and this is indicated by block 572.

At block 574, settings resolver identifier component 526 identifies thesettings resolver for the selected WMAs in each regime zone defined byregimes zone boundary definition component 524. As discussed above, theregime zone resolver can be a human resolver 576, an artificialintelligence or machine learning system resolver 578, a resolver 580based on predicted or historic quality for each competing targetsetting, a rules-based resolver 582, a performance criteria-basedresolver 584, or other resolvers 586.

At block 588, WMA selector 486 determines whether there are more WMAs orsets of WMAs to process. If additional WMAs or sets of WMAs areremaining to be processed, processing reverts to block 436 where thenext WMA or set of WMAs for which control zones and regime zones are tobe defined is selected. When no additional WMAs or sets of WMAs forwhich control zones or regime zones are to be generated are remaining,processing moves to block 590 where control zone generator 213 outputs amap with control zones, target settings, regime zones, and settingsresolvers for each of the WMAs or sets of WMAs. As discussed above, theoutputted map can be presented to operator 260 or another user; theoutputted map can be provided to control system 214; or the outputtedmap can be output in other ways.

FIG. 8 illustrates one example of the operation of control system 214 incontrolling agricultural harvester 100 based upon a map that is outputby control zone generator 213. Thus, at block 592, control system 214receives a map of the worksite. In some instances, the map can be afunctional predictive map that may include control zones and regimezones, as represented by block 594. In some instances, the received mapmay be a functional predictive map that excludes control zones andregime zones. Block 596 indicates an example in which the received mapof the worksite can be a prior information map having control zones andregime zones identified on it. Block 598 indicates an example in whichthe received map can include multiple different maps or multipledifferent map layers. Block 610 indicates an example in which thereceived map can take other forms as well.

At block 612, control system 214 receives a sensor signal fromgeographic position sensor 204. The sensor signal from geographicposition sensor 204 can include data that indicates the geographiclocation 614 of agricultural harvester 100, the speed 616 ofagricultural harvester 100, the heading 618 or agricultural harvester100, or other information 620. At block 622, zone controller 247 selectsa regime zone, and, at block 624, zone controller 247 selects a controlzone on the map based on the geographic position sensor signal. At block626, zone controller 247 selects a WMA or a set of WMAs to becontrolled. At block 628, zone controller 247 obtains one or more targetsettings for the selected WMA or set of WMAs. The target settings thatare obtained for the selected WMA or set of WMAs may come from a varietyof different sources. For instance, block 630 shows an example in whichone or more of the target settings for the selected WMA or set of WMAsis based on an input from the control zones on the map of the worksite.Block 632 shows an example in which one or more of the target settingsis obtained from human inputs from operator 260 or another user. Block634 shows an example in which the target settings are obtained from anin-situ sensor 208. Block 636 shows an example in which the one or moretarget settings is obtained from one or more sensors on other machinesworking in the same field either concurrently with agriculturalharvester 100 or from one or more sensors on machines that worked in thesame field in the past. Block 638 shows an example in which the targetsettings are obtained from other sources as well.

At block 640, zone controller 247 accesses the settings resolver for theselected regime zone and controls the settings resolver to resolvecompeting target settings into a resolved target setting. As discussedabove, in some instances, the settings resolver may be a human resolverin which case zone controller 247 controls operator interface mechanisms218 to present the competing target settings to operator 260 or anotheruser for resolution. In some instances, the settings resolver may be aneural network or other artificial intelligence or machine learningsystem, and zone controller 247 submits the competing target settings tothe neural network, artificial intelligence, or machine learning systemfor selection. In some instances, the settings resolver may be based ona predicted or historic quality metric, on threshold rules, or onlogical components. In any of these latter examples, zone controller 247executes the settings resolver to obtain a resolved target setting basedon the predicted or historic quality metric, based on the thresholdrules, or with the use of the logical components.

At block 642, with zone controller 247 having identified the resolvedtarget setting, zone controller 247 provides the resolved target settingto other controllers in control system 214, which generate and applycontrol signals to the selected WMA or set of WMAs based upon theresolved target setting. For instance, where the selected WMA is amachine or header actuator 248, zone controller 247 provides theresolved target setting to settings controller 232 or header/realcontroller 238 or both to generate control signals based upon theresolved target setting, and those generated control signals are appliedto the machine or header actuators 248. At block 644, if additional WMAsor additional sets of WMAs are to be controlled at the currentgeographic location of the agricultural harvester 100 (as detected atblock 612), then processing reverts to block 626 where the next WMA orset of WMAs is selected. The processes represented by blocks 626 through644 continue until all of the WMAs or sets of WMAs to be controlled atthe current geographical location of the agricultural harvester 100 havebeen addressed. If no additional WMAs or sets of WMAs are to becontrolled at the current geographic location of the agriculturalharvester 100 remain, processing proceeds to block 646 where zonecontroller 247 determines whether additional control zones to beconsidered exist in the selected regime zone. If additional controlzones to be considered exist, processing reverts to block 624 where anext control zone is selected. If no additional control zones areremaining to be considered, processing proceeds to block 648 where adetermination as to whether additional regime zones are remaining to beconsider. Zone controller 247 determines whether additional regime zonesare remaining to be considered. If additional regimes zone are remainingto be considered, processing reverts to block 622 where a next regimezone is selected.

At block 650, zone controller 247 determines whether the operation thatagricultural harvester 100 is performing is complete. If not, the zonecontroller 247 determines whether a control zone criterion has beensatisfied to continue processing, as indicated by block 652. Forinstance, as mentioned above, control zone definition criteria mayinclude criteria defining when a control zone boundary may be crossed bythe agricultural harvester 100. For example, whether a control zoneboundary may be crossed by the agricultural harvester 100 may be definedby a selected time period, meaning that agricultural harvester 100 isprevented from crossing a zone boundary until a selected amount of timehas transpired. In that case, at block 652, zone controller 247determines whether the selected time period has elapsed. Additionally,zone controller 247 can perform processing continually. Thus, zonecontroller 247 does not wait for any particular time period beforecontinuing to determine whether an operation of the agriculturalharvester 100 is completed. At block 652, zone controller 247 determinesthat it is time to continue processing, then processing continues atblock 612 where zone controller 247 again receives an input fromgeographic position sensor 204. It will also be appreciated that zonecontroller 247 can control the WMAs and sets of WMAs simultaneouslyusing a multiple-input, multiple-output controller instead ofcontrolling the WMAs and sets of WMAs sequentially.

FIG. 9 is a block diagram showing one example of an operator interfacecontroller 231. In an illustrated example, operator interface controller231 includes operator input command processing system 654, othercontroller interaction system 656, speech processing system 658, andaction signal generator 660. Operator input command processing system654 includes speech handling system 662, touch gesture handling system664, and other items 666. Other controller interaction system 656includes controller input processing system 668 and controller outputgenerator 670. Speech processing system 658 includes trigger detector672, recognition component 674, synthesis component 676, naturallanguage understanding system 678, dialog management system 680, andother items 682. Action signal generator 660 includes visual controlsignal generator 684, audio control signal generator 686, haptic controlsignal generator 688, and other items 690. Before describing operationof the example operator interface controller 231 shown in FIG. 9 inhandling various operator interface actions, a brief description of someof the items in operator interface controller 231 and the associatedoperation thereof is first provided.

Operator input command processing system 654 detects operator inputs onoperator interface mechanisms 218 and processes those inputs forcommands. Speech handling system 662 detects speech inputs and handlesthe interactions with speech processing system 658 to process the speechinputs for commands. Touch gesture handling system 664 detects touchgestures on touch sensitive elements in operator interface mechanisms218 and processes those inputs for commands.

Other controller interaction system 656 handles interactions with othercontrollers in control system 214. Controller input processing system668 detects and processes inputs from other controllers in controlsystem 214, and controller output generator 670 generates outputs andprovides those outputs to other controllers in control system 214.Speech processing system 658 recognizes speech inputs, determines themeaning of those inputs, and provides an output indicative of themeaning of the spoken inputs. For instance, speech processing system 658may recognize a speech input from operator 260 as a settings changecommand in which operator 260 is commanding control system 214 to changea setting for a controllable subsystem 216. In such an example, speechprocessing system 658 recognizes the content of the spoken command,identifies the meaning of that command as a settings change command, andprovides the meaning of that input back to speech handling system 662.Speech handling system 662, in turn, interacts with controller outputgenerator 670 to provide the commanded output to the appropriatecontroller in control system 214 to accomplish the spoken settingschange command.

Speech processing system 658 may be invoked in a variety of differentways. For instance, in one example, speech handling system 662continuously provides an input from a microphone (being one of theoperator interface mechanisms 218) to speech processing system 658. Themicrophone detects speech from operator 260, and the speech handlingsystem 662 provides the detected speech to speech processing system 658.Trigger detector 672 detects a trigger indicating that speech processingsystem 658 is invoked. In some instances, when speech processing system658 is receiving continuous speech inputs from speech handling system662, speech recognition component 674 performs continuous speechrecognition on all speech spoken by operator 260. In some instances,speech processing system 658 is configured for invocation using a wakeupword. That is, in some instances, operation of speech processing system658 may be initiated based on recognition of a selected spoken word,referred to as the wakeup word. In such an example, where recognitioncomponent 674 recognizes the wakeup word, the recognition component 674provides an indication that the wakeup word has been recognized totrigger detector 672. Trigger detector 672 detects that speechprocessing system 658 has been invoked or triggered by the wakeup word.In another example, speech processing system 658 may be invoked by anoperator 260 actuating an actuator on a user interface mechanism, suchas by touching an actuator on a touch sensitive display screen, bypressing a button, or by providing another triggering input. In such anexample, trigger detector 672 can detect that speech processing system658 has been invoked when a triggering input via a user interfacemechanism is detected. Trigger detector 672 can detect that speechprocessing system 658 has been invoked in other ways as well.

Once speech processing system 658 is invoked, the speech input fromoperator 260 is provided to speech recognition component 674. Speechrecognition component 674 recognizes linguistic elements in the speechinput, such as words, phrases, or other linguistic units. Naturallanguage understanding system 678 identifies a meaning of the recognizedspeech. The meaning may be a natural language output, a command outputidentifying a command reflected in the recognized speech, a value outputidentifying a value in the recognized speech, or any of a wide varietyof other outputs that reflect the understanding of the recognizedspeech. For example, the natural language understanding system 678 andspeech processing system 568, more generally, may understand of themeaning of the recognized speech in the context of agriculturalharvester 100.

In some examples, speech processing system 658 can also generate outputsthat navigate operator 260 through a user experience based on the speechinput. For instance, dialog management system 680 may generate andmanage a dialog with the user in order to identify what the user wishesto do. The dialog may disambiguate a user's command; identify one ormore specific values that are needed to carry out the user's command; orobtain other information from the user or provide other information tothe user or both. Synthesis component 676 may generate speech synthesiswhich can be presented to the user through an audio operator interfacemechanism, such as a speaker. Thus, the dialog managed by dialogmanagement system 680 may be exclusively a spoken dialog or acombination of both a visual dialog and a spoken dialog.

Action signal generator 660 generates action signals to control operatorinterface mechanisms 218 based upon outputs from one or more of operatorinput command processing system 654, other controller interaction system656, and speech processing system 658. Visual control signal generator684 generates control signals to control visual items in operatorinterface mechanisms 218. The visual items may be lights, a displayscreen, warning indicators, or other visual items. Audio control signalgenerator 686 generates outputs that control audio elements of operatorinterface mechanisms 218. The audio elements include a speaker, audiblealert mechanisms, horns, or other audible elements. Haptic controlsignal generator 688 generates control signals that are output tocontrol haptic elements of operator interface mechanisms 218. The hapticelements include vibration elements that may be used to vibrate, forexample, the operator's seat, the steering wheel, pedals, or joysticksused by the operator. The haptic elements may include tactile feedbackor force feedback elements that provide tactile feedback or forcefeedback to the operator through operator interface mechanisms. Thehaptic elements may include a wide variety of other haptic elements aswell.

FIG. 10 is a flow diagram illustrating one example of the operation ofoperator interface controller 231 in generating an operator interfacedisplay on an operator interface mechanism 218, which can include atouch sensitive display screen. FIG. 10 also illustrates one example ofhow operator interface controller 231 can detect and process operatorinteractions with the touch sensitive display screen.

At block 692, operator interface controller 231 receives a map. Block694 indicates an example in which the map is a functional predictivemap, and block 696 indicates an example in which the map is another typeof map. At block 698, operator interface controller 231 receives aninput from geographic position sensor 204 identifying the geographiclocation of the agricultural harvester 100. As indicated in block 700,the input from geographic position sensor 204 can include the heading,along with the location, of agricultural harvester 100. Block 702indicates an example in which the input from geographic position sensor204 includes the speed of agricultural harvester 100, and block 704indicates an example in which the input from geographic position sensor204 includes other items.

At block 706, visual control signal generator 684 in operator interfacecontroller 231 controls the touch sensitive display screen in operatorinterface mechanisms 218 to generate a display showing all or a portionof a field represented by the received map. Block 708 indicates that thedisplayed field can include a current position marker showing a currentposition of the agricultural harvester 100 relative to the field. Block710 indicates an example in which the displayed field includes a nextwork unit marker that identifies a next work unit (or area on the field)in which agricultural harvester 100 will be operating. Block 712indicates an example in which the displayed field includes an upcomingarea display portion that displays areas that are yet to be processed byagricultural harvester 100, and block 714 indicates an example in whichthe displayed field includes previously visited display portions thatrepresent areas of the field that agricultural harvester 100 has alreadyprocessed. Block 716 indicates an example in which the displayed fielddisplays various characteristics of the field having georeferencedlocations on the map. For instance, if the received map is a predictiveloss map, such as functional predictive loss map 420, the displayedfield may show the different categories of the level of loss existing inthe field georeferenced within the displayed field. The mappedcharacteristics can be shown in the previously visited areas (as shownin block 714), in the upcoming areas (as shown in block 712), and in thenext work unit (as shown in block 710). Block 718 indicates an examplein which the displayed field includes other items as well.

FIG. 11 is a pictorial illustration showing one example of a userinterface display 720 that can be generated on a touch sensitive displayscreen. In other implementations, the user interface display 720 may begenerated on other types of displays. The touch sensitive display screenmay be mounted in the operator compartment of agricultural harvester 100or on the mobile device or elsewhere. User interface display 720 will bedescribed prior to continuing with the description of the flow diagramshown in FIG. 10 .

In the example shown in FIG. 11 , user interface display 720 illustratesthat the touch sensitive display screen includes a display feature foroperating a microphone 722 and a speaker 724. Thus, the touch sensitivedisplay may be communicably coupled to the microphone 722 and thespeaker 724. Block 726 indicates that the touch sensitive display screencan include a wide variety of user interface control actuators, such asbuttons, keypads, soft keypads, links, icons, switches, etc. Theoperator 260 can actuator the user interface control actuators toperform various functions.

In the example shown in FIG. 11 , user interface display 720 includes afield display portion 728 that displays at least a portion of the fieldin which the agricultural harvester 100 is operating. The field displayportion 728 is shown with a current position marker 708 that correspondsto a current position of agricultural harvester 100 in the portion ofthe field shown in field display portion 728. In one example, theoperator may control the touch sensitive display in order to zoom intoportions of field display portion 728 or to pan or scroll the fielddisplay portion 728 to show different portions of the field. A next workunit 730 is shown as an area of the field directly in front of thecurrent position marker 708 of agricultural harvester 100. The currentposition marker 708 may also be configured to identify the direction oftravel of agricultural harvester 100, a speed of travel of agriculturalharvester 100 or both. In FIG. 13 , the shape of the current positionmarker 708 provides an indication as to the orientation of theagricultural harvester 100 within the field which may be used as anindication of a direction of travel of the agricultural harvester 100.

The size of the next work unit 730 marked on field display portion 728may vary based upon a wide variety of different criteria. For instance,the size of next work unit 730 may vary based on the speed of travel ofagricultural harvester 100. Thus, when the agricultural harvester 100 istraveling faster, then the area of the next work unit 730 may be largerthan the area of next work unit 730 if agricultural harvester 100 istraveling more slowly. In another example, the size of the next workunit 730 may vary based on the dimensions of the agricultural harvester100, including equipment on agricultural harvester 100 (such as header102). For example, the width of the next work unit 730 may vary based ona width of header 102. Field display portion 728 is also showndisplaying previously visited area 714 and upcoming areas 712.Previously visited areas 714 represent areas that are already harvestedwhile upcoming areas 712 represent areas that still need to beharvested. The field display portion 728 is also shown displayingdifferent characteristics of the field. In the example illustrated inFIG. 11 , the map that is being displayed is a predictive loss map, suchas functional predictive loss map 420. Therefore, a plurality ofdifferent loss level markers are displayed on field display portion 728.There are a set of loss level display markers 732 shown in the alreadyvisited areas 714. There are also a set of loss level display markers732 shown in the upcoming areas 712, and there are a set of loss leveldisplay markers 732 shown in the next work unit 730. FIG. 11 shows thatthe loss level display markers 732 are made up of different symbols thatindicate an area of similar loss level. In the example shown in FIG. 3 ,the ! symbol represents areas of high loss level; the * symbolrepresents areas of medium loss level; and the #symbol represents anarea of low loss level. Thus, the field display portion 728 showsdifferent measured or predicted values (or characteristics indicated bythe values) that are located at different areas within the field andrepresents those measured or predicted values (or characteristicsindicated by the values) with a variety of display markers 732. Asshown, the field display portion 728 includes display markers,particularly loss level display markers 732 in the illustrated exampleof FIG. 11 , at particular locations associated with particularlocations on the field being displayed. In some instances, each locationof the field may have a display marker associated therewith. Thus, insome instances, a display marker may be provided at each location of thefield display portion 728 to identify the nature of the characteristicbeing mapped for each particular location of the field. Consequently,the present disclosure encompasses providing a display marker, such asthe loss level display marker 732 (as in the context of the presentexample of FIG. 11 ), at one or more locations on the field displayportion 728 to identify the nature, degree, etc., of the characteristicbeing displayed, thereby identifying the characteristic at thecorresponding location in the field being displayed. As describedearlier, the display markers 732 may be made up of different symbols,and, as described below, the symbols may be any display feature such asdifferent colors, shapes, patterns, intensities, text, icons, or otherdisplay features.

In other examples, the map being displayed may be one or more of themaps described herein, including information maps, prior informationmaps, the functional predictive maps, such as predictive maps orpredictive control zone maps, or a combination thereof. Thus, themarkers and characteristics being displayed will correlate to theinformation, data, characteristics, and values provided by the one ormore maps being displayed.

In the example of FIG. 11 , user interface display 720 also has acontrol display portion 738. Control display portion 738 allows theoperator to view information and to interact with user interface display720 in various ways.

The actuators and display elements in portion 738 may be displayed as,for example, individual items, fixed lists, scrollable lists, drop downmenus, or drop down lists. In the example shown in FIG. 11 , displayportion 738 shows information for the three different loss levels thatcorrespond to the three symbols mentioned above. Display portion 738also includes a set of touch sensitive actuators with which the operator260 can interact by touch. For example, the operator 260 may touch thetouch sensitive actuators with a finger to activate the respective touchsensitive actuator.

As shown in FIG. 11 , display portion 738 includes an interactive flagdisplay portion, indicated generally at 741. Interactive flag displayportion 741 includes a flag column 739 that shows flags that have beenautomatically or manually set. Flag actuator 740 allows operator 260 tomark a location, such as the current location of the agriculturalharvester, or another location on the field designated by the operatorand add information indicating the loss level found at the currentlocation. For instance, when the operator 260 actuates the flag actuator740 by touching the flag actuator 740, touch gesture handling system 664in operator interface controller 231 identifies the current location asone where agricultural harvester 100 encountered high loss level. Whenthe operator 260 touches the button 742, touch gesture handling system664 identifies the current location as a location where agriculturalharvester 100 encountered medium loss level. When the operator 260touches the button 744, touch gesture handling system 664 identifies thecurrent location as a location where agricultural harvester 100encountered low loss level. Upon actuation of one of the flag actuators740, 742, or 744, touch gesture handling system 664 can control visualcontrol signal generator 684 to add a symbol corresponding to theidentified loss level on field display portion 728 at a location theuser identifies. In this way, areas of the field where the predictedvalue did not accurately represent an actual value can be marked forlater analysis, and can also be used in machine learning. In otherexamples, the operator may designate areas ahead of or around theagricultural harvester 100 by actuating one of the flag actuators 740,742, or 744 such that control of the agricultural harvester 100 can beundertaken based on the value designated by the operator 260.

Display portion 738 also includes an interactive marker display portion,indicated generally at 743. Interactive marker display portion 743includes a symbol column 746 that displays the symbols corresponding toeach category of values or characteristics (in the case of FIG. 11 ,loss level) that is being tracked on the field display portion 728.Display portion 738 also includes an interactive designator displayportion, indicated generally at 745. Interactor designator displayportion 745 includes a designator column 748 that shows the designator(which may be a textual designator or other designator) identifying thecategory of values or characteristics (in the case of FIG. 11 , losslevel). Without limitation, the symbols in symbol column 746 and thedesignators in designator column 748 can include any display featuresuch as different colors, shapes, patterns, intensities, text, icons, orother display features, and can be customizable by interaction of anoperator of agricultural harvester 100.

Display portion 738 also includes an interactive value display portion,indicated generally at 747. Interactive value display portion 747includes a value display column 750 that displays selected values. Theselected values correspond to the characteristics or values beingtracked or displayed, or both, on field display portion 728. Theselected values can be selected by an operator of the agriculturalharvester 100. The selected values in value display column 750 define arange of values or a value by which other values, such as predictedvalues, are to be classified. Thus, in the example in FIG. 11 , apredicted or measured loss level meeting or greater than 1.5bushels/acre is classified as “high loss level”, and a predicted ormeasured loss level meeting or less than 0.5 bushels/acre is classifiedas “low loss level.” In some examples, the selected values may include arange, such that a predicted or measured value that is within the rangeof the selected value will be classified under the correspondingdesignator. As shown in FIG. 11 , “medium loss level” includes a rangeof 0.51 bushels/acre to 1.49 bushels/acre such that a predicted ormeasured loss level falling within the range 0.51-to-1.49 bushels/acreis classified as “medium loss level”. The selected values in valuedisplay column 750 are adjustable by an operator of agriculturalharvester 100. In one example, the operator 260 can select theparticular part of field display portion 728 for which the values incolumn 750 are to be displayed. Thus, the values in column 750 cancorrespond to values in display portions 712, 714 or 730.

Display portion 738 also includes an interactive threshold displayportion, indicated generally at 749. Interactive threshold displayportion 749 includes a threshold value display column 752 that displaysaction threshold values. Action threshold values in column 752 may bethreshold values corresponding to the selected values in value displaycolumn 750. If the predicted or measured values of characteristics beingtracked or displayed, or both, satisfy the corresponding actionthreshold values in threshold value display column 752, then controlsystem 214 takes the action identified in column 754. In some instances,a measured or predicted value may satisfy a corresponding actionthreshold value by meeting or exceeding the corresponding actionthreshold value. In one example, operator 260 can select a thresholdvalue, for example, in order to change the threshold value by touchingthe threshold value in threshold value display column 752. Onceselected, the operator 260 may change the threshold value. The thresholdvalues in column 752 can be configured such that the designated actionis performed when the measured or predicted value of the characteristicexceeds the threshold value, equals the threshold value, or is less thanthe threshold value. In some instances, the threshold value mayrepresent a range of values, or range of deviation from the selectedvalues in value display column 750, such that a predicted or measuredcharacteristic value that meets or falls within the range satisfies thethreshold value. For instance, in the example of FIG. 11 , a predictedvalue that falls within 10% of 1.5 bushels/acre will satisfy thecorresponding action threshold value (of within 10% of 1.5 bushels/acre)and an action, such as reducing the cleaning fan speed, will be taken bycontrol system 214. In other examples, the threshold values in columnthreshold value display column 752 are separate from the selected valuesin value display column 750, such that the values in value displaycolumn 750 define the classification and display of predicted ormeasured values, while the action threshold values define when an actionis to be taken based on the measured or predicted values. For example,while a predicted or measured loss value of 1.0 bushels/acre may bedesignated as a “medium loss level” for purposes of classification anddisplay, the action threshold value may be 1.2 bushels/acre such that noaction will be taken until the loss value satisfies the threshold value.In other examples, the threshold values in threshold value displaycolumn 752 may include distances or times. For instance, in the exampleof a distance, the threshold value may be a threshold distance from thearea of the field where the measured or predicted value is georeferencedthat the agricultural harvester 100 must be before an action is taken.For example, a threshold distance value of 10 feet would mean that anaction will be taken when the agricultural harvester is at or within 10feet of the area of the field where the measured or predicted value isgeoreferenced. In an example where the threshold value is time, thethreshold value may be a threshold time for the agricultural harvester100 to reach the area of the field where the measured or predictivevalue is georeferenced. For instance, a threshold value of 5 secondswould mean that an action will be taken when the agricultural harvester100 is 5 seconds away from the area of the field where the measured orpredicted value is georeferenced. In such an example, the currentlocation and travel speed of the agricultural harvester can be accountedfor.

Display portion 738 also includes an interactive action display portion,indicated generally at 751. Interactive action display portion 751includes an action display column 754 that displays action identifiersthat indicated actions to be taken when a predicted or measured valuesatisfies an action threshold value in threshold value display column752. Operator 260 can touch the action identifiers in column 754 tochange the action that is to be taken. When a threshold is satisfied, anaction may be taken. For instance, at the bottom of column 754, anincrease cleaning fan speed action and a reduce cleaning fan speedaction are identified as actions that will be taken if the measured orpredicted value in meets the threshold value in column 752. In someexamples, then a threshold is met, multiple actions may be taken. Forinstance, a cleaning fan speed may be adjusted, a threshing rotor speedmay be adjusted, and a concave clearance may be adjusted in response toa threshold being satisfied.

The actions that can be set in column 754 can be any of a wide varietyof different types of actions. For example, the actions can include akeep out action which, when executed, inhibits agricultural harvester100 from further harvesting in an area. The actions can include a speedchange action which, when executed, changes the travel speed ofagricultural harvester 100 through the field. The actions can include asetting change action for changing a setting of an internal actuator oranother WMA or set of WMAs or for implementing a settings change actionthat changes a setting of a threshing rotor speed, a cleaning fan speed,a position (e.g., tilt, height, roll, etc.) of the header, along withvarious other settings. These are examples only, and a wide variety ofother actions are contemplated herein.

The items shown on user interface display 720 can be visuallycontrolled. Visually controlling the interface display 720 may beperformed to capture the attention of operator 260. For instance, thedisplay elements can be controlled to modify the intensity, color, orpattern with which the display elements are displayed. Additionally, thedisplay elements may be controlled to flash. The described alterationsto the visual appearance of the display elements are provided asexamples. Consequently, other aspects of the visual appearance of thedisplay elements may be altered. Therefore, the display elements can bemodified under various circumstances in a desired manner in order, forexample, to capture the attention of operator 260. Additionally, while aparticular number of items are shown on user interface display 720, thisneed not be the case. In other examples, more or less items, includingmore or less of a particular item can be included on user interfacedisplay 720.

Returning now to the flow diagram of FIG. 10 , the description of theoperation of operator interface controller 231 continues. At block 760,operator interface controller 231 detects an input setting a flag andcontrols the touch sensitive user interface display 720 to display theflag on field display portion 728. The detected input may be an operatorinput, as indicated at 762, or an input from another controller, asindicated at 764. At block 766, operator interface controller 231detects an in-situ sensor input indicative of a measured characteristicof the field from one of the in-situ sensors 208. At block 768, visualcontrol signal generator 684 generates control signals to control userinterface display 720 to display actuators for modifying user interfacedisplay 720 and for modifying machine control. For instance, block 770represents that one or more of the actuators for setting or modifyingthe values in columns 739, 746, and 748 can be displayed. Thus, the usercan set flags and modify characteristics of those flags. For example, auser can modify the loss levels and loss level designators correspondingto the flags. Block 772 represents that action threshold values incolumn 752 are displayed. Block 776 represents that the actions incolumn 754 are displayed, and block 778 represents that the selectedvalue in column 750 is displayed. Block 780 indicates that a widevariety of other information and actuators can be displayed on userinterface display 720 as well.

At block 782, operator input command processing system 654 detects andprocesses operator inputs corresponding to interactions with the userinterface display 720 performed by the operator 260. Where the userinterface mechanism on which user interface display 720 is displayed isa touch sensitive display screen, interaction inputs with the touchsensitive display screen by the operator 260 can be touch gestures 784.In some instances, the operator interaction inputs can be inputs using apoint and click device 786 or other operator interaction inputs 788.

At block 790, operator interface controller 231 receives signalsindicative of an alert condition. For instance, block 792 indicates thatsignals may be received by controller input processing system 668indicating that detected or predicted values satisfy thresholdconditions present in column 752. As explained earlier, the thresholdconditions may include values being below a threshold, at a threshold,or above a threshold. Block 794 shows that action signal generator 660can, in response to receiving an alert condition, alert the operator 260by using visual control signal generator 684 to generate visual alerts,by using audio control signal generator 686 to generate audio alerts, byusing haptic control signal generator 688 to generate haptic alerts, orby using any combination of these. Similarly, as indicated by block 796,controller output generator 670 can generate outputs to othercontrollers in control system 214 so that those controllers perform thecorresponding action identified in column 754. Block 798 shows thatoperator interface controller 231 can detect and process alertconditions in other ways as well.

Block 900 shows that speech handling system 662 may detect and processinputs invoking speech processing system 658. Block 902 shows thatperforming speech processing may include the use of dialog managementsystem 680 to conduct a dialog with the operator 260. Block 904 showsthat the speech processing may include providing signals to controlleroutput generator 670 so that control operations are automaticallyperformed based upon the speech inputs.

Table 1, below, shows an example of a dialog between operator interfacecontroller 231 and operator 260. In Table 1, operator 260 uses a triggerword or a wakeup word that is detected by trigger detector 672 to invokespeech processing system 658. In the example shown in Table 1, thewakeup word is “Johnny”.

TABLE 1 Operator: “Johnny, tell me about the loss level” OperatorInterface Controller: “Current loss level is high.”

Table 2 shows an example in which speech synthesis component 676provides an output to audio control signal generator 686 to provideaudible updates on an intermittent or periodic basis. The intervalbetween updates may be time-based, such as every five minutes, orcoverage or distance-based, such as every five acres, orexception-based, such as when a measured value is greater than athreshold value.

TABLE 2 Operator Interface Controller: “Over last 10 minutes, loss levelhas been high.” Operator Interface Controller: “Next 1 acre predictedloss level is medium.”

Operator Interface Controller: “Over last 10 minutes, loss level hasbeen high.”

Operator Interface Controller: “Next 1 acre predicted loss level ismedium.”

The example shown in Table 3 illustrates that some actuators or userinput mechanisms on the touch sensitive display 720 can be supplementedwith speech dialog. The example in Table 3 illustrates that actionsignal generator 660 can generate action signals to automatically mark ahigh loss level area in the field being harvested.

TABLE 3 Human: “Johnny, mark high loss level area.” Operator InterfaceController: “High loss level area marked.”

The example shown in Table 4 illustrates that action signal generator660 can conduct a dialog with operator 260 to begin and end marking of ahigh loss level area.

TABLE 4 Human: “Johnny, start marking high loss level area.” OperatorInterface Controller: “Marking high loss level area.” Human: “Johnny,stop marking high loss level area.” Operator Interface Controller: “Highloss level area marking stopped.”

The example shown in Table 5 illustrates that action signal generator160 can generate signals to mark a low loss level area in a differentway than those shown in Tables 3 and 4.

TABLE 5 Human: “Johnny, mark next 100 feet as low loss level area.”Operator Interface Controller: “Next 100 feet marked as a low loss levelarea.”

Returning again to FIG. 10 , block 906 illustrates that operatorinterface controller 231 can detect and process conditions foroutputting a message or other information in other ways as well. Forinstance, other controller interaction system 656 can detect inputs fromother controllers indicating that alerts or output messages should bepresented to operator 260. Block 908 shows that the outputs can be audiomessages. Block 910 shows that the outputs can be visual messages, andblock 912 shows that the outputs can be haptic messages. Until operatorinterface controller 231 determines that the current harvestingoperation is completed, as indicated by block 914, processing reverts toblock 698 where the geographic location of harvester 100 is updated andprocessing proceeds as described above to update user interface display720.

Once the operation is complete, then any desired values that aredisplayed, or have been displayed on user interface display 720, can besaved. Those values can also be used in machine learning to improvedifferent portions of predictive model generator 210, predictive mapgenerator 212, control zone generator 213, control algorithms, or otheritems. Saving the desired values is indicated by block 916. The valuescan be saved locally on agricultural harvester 100, or the values can besaved at a remote server location or sent to another remote system.

It can thus be seen that an information map is obtained by anagricultural harvester that shows characteristic values at differentgeographic locations of a field being harvested. An in-situ sensor onthe harvester senses a characteristic as the agricultural harvestermoves through the field. A predictive map generator generates apredictive map that includes control values for different locations inthe field based on the values in the information map and thecharacteristic sensed by the in-situ sensor. A control system controlscontrollable subsystem based on the control values in the predictivemap.

A control value is a value upon which an action can be based. A controlvalue, as described herein, can include any value (or characteristicsindicated by or derived from the value) that may be used in the controlof agricultural harvester 100. A control value can be any valueindicative of an agricultural characteristic. A control value can be apredicted value, a measured value, or a detected value. A control valuemay include any of the values provided by a map, such as any of the mapsdescribed herein, for instance, a control value can be a value providedby an information map, a value provided by prior information map, or avalue provided predictive map, such as a functional predictive map. Acontrol value can also include any of the characteristics indicated byor derived from the values detected by any of the sensors describedherein. In other examples, a control value can be provided by anoperator of the agricultural machine, such as a command input by anoperator of the agricultural machine.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. Theprocessors and servers are functional parts of the systems or devices towhich the processors and servers belong and are activated by andfacilitate the functionality of the other components or items in thosesystems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, the user actuatable operator interface mechanisms can beactuated using operator interface mechanisms such as a point and clickdevice, such as a track ball or mouse, hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc., a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the user actuatable operator interface mechanisms are displayed isa touch sensitive screen, the user actuatable operator interfacemechanisms can be actuated using touch gestures. Also, user actuatableoperator interface mechanisms can be actuated using speech commandsusing speech recognition functionality. Speech recognition may beimplemented using a speech detection device, such as a microphone, andsoftware that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic, and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, including but not limited toartificial intelligence components, such as neural networks, some ofwhich are described below, that perform the functions associated withthose systems, components, logic, or interactions. In addition, any orall of the systems, components, logic and interactions may beimplemented by software that is loaded into a memory and is subsequentlyexecuted by a processor or server or other computing component, asdescribed below. Any or all of the systems, components, logic andinteractions may also be implemented by different combinations ofhardware, software, firmware, etc., some examples of which are describedbelow. These are some examples of different structures that may be usedto implement any or all of the systems, components, logic andinteractions described above. Other structures may be used as well.

FIG. 12 is a block diagram of agricultural harvester 600, which may besimilar to agricultural harvester 100 shown in FIG. 2 . The agriculturalharvester 600 communicates with elements in a remote server architecture500. In some examples, remote server architecture 500 providescomputation, software, data access, and storage services that do notrequire end-user knowledge of the physical location or configuration ofthe system that delivers the services. In various examples, remoteservers may deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers maydeliver applications over a wide area network and may be accessiblethrough a web browser or any other computing component. Software orcomponents shown in FIG. 2 as well as data associated therewith, may bestored on servers at a remote location. The computing resources in aremote server environment may be consolidated at a remote data centerlocation, or the computing resources may be dispersed to a plurality ofremote data centers. Remote server infrastructures may deliver servicesthrough shared data centers, even though the services appear as a singlepoint of access for the user. Thus, the components and functionsdescribed herein may be provided from a remote server at a remotelocation using a remote server architecture. Alternatively, thecomponents and functions may be provided from a server, or thecomponents and functions can be installed on client devices directly, orin other ways.

In the example shown in FIG. 12 , some items are similar to those shownin FIG. 2 and those items are similarly numbered. FIG. 12 specificallyshows that predictive model generator 210 or predictive map generator212, or both, may be located at a server location 502 that is remotefrom the agricultural harvester 600. Therefore, in the example shown inFIG. 12 , agricultural harvester 600 accesses systems through remoteserver location 502.

FIG. 12 also depicts another example of a remote server architecture.FIG. 12 shows that some elements of FIG. 2 may be disposed at a remoteserver location 502 while others may be located elsewhere. By way ofexample, data store 202 may be disposed at a location separate fromlocation 502 and accessed via the remote server at location 502.Regardless of where the elements are located, the elements can beaccessed directly by agricultural harvester 600 through a network suchas a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users, or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated, or manual information collectionsystem. As the combine harvester 600 comes close to the machinecontaining the information collection system, such as a fuel truck priorto fueling, the information collection system collects the informationfrom the combine harvester 600 using any type of ad-hoc wirelessconnection. The collected information may then be forwarded to anothernetwork when the machine containing the received information reaches alocation where wireless telecommunication service coverage or otherwireless coverage—is available. For instance, a fuel truck may enter anarea having wireless communication coverage when traveling to a locationto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationmay be stored on the agricultural harvester 600 until the agriculturalharvester 600 enters an area having wireless communication coverage. Theagricultural harvester 600, itself, may send the information to anothernetwork.

It will also be noted that the elements of FIG. 2 , or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 500 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 13 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of agricultural harvester 100 for use ingenerating, processing, or displaying the maps discussed above. FIGS.14-15 are examples of handheld or mobile devices.

FIG. 13 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 2 , that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 14 shows one example in which device 16 is a tablet computer 600.In FIG. 14 , computer 601 is shown with user interface display screen602. Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 600 may also usean on-screen virtual keyboard. Of course, computer 601 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 601 may also illustratively receive voice inputs as well.

FIG. 15 is similar to FIG. 14 except that the device is a smart phone71. Smart phone 71 has a touch sensitive display 73 that displays iconsor tiles or other user input mechanisms 75. Mechanisms 75 can be used bya user to run applications, make calls, perform data transferoperations, etc. In general, smart phone 71 is built on a mobileoperating system and offers more advanced computing capability andconnectivity than a feature phone.

Note that other forms of the devices 16 are possible.

FIG. 16 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference to FIG. 16 , an example systemfor implementing some embodiments includes a computing device in theform of a computer 810 programmed to operate as discussed above.Components of computer 810 may include, but are not limited to, aprocessing unit 820 (which can comprise processors or servers fromprevious FIGS.), a system memory 830, and a system bus 821 that couplesvarious system components including the system memory to the processingunit 820. The system bus 821 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Memoryand programs described with respect to FIG. 2 can be deployed incorresponding portions of FIG. 16 .

Computer 810 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 831 and random access memory (RAM) 832. A basic input/outputsystem 833 (BIOS), containing the basic routines that help to transferinformation between elements within computer 810, such as duringstart-up, is typically stored in ROM 831. RAM 832 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 820. By way ofexample, and not limitation, FIG. 16 illustrates operating system 834,application programs 835, other program modules 836, and program data837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 16 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 16 , provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 16 , for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 16 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

-   -   Example 1 is an agricultural work machine, comprising:    -   a communication system that receives an information map that        includes values of a first agricultural characteristic        corresponding to different geographic locations in a field;    -   a geographic position sensor that detects a geographic location        of the agricultural work machine;    -   an in-situ sensor that detects a value of a second agricultural        characteristic indicative of a characteristic of processed        material corresponding to the geographic location;    -   a predictive model generator that generates a predictive        agricultural model that models a relationship between the first        agricultural characteristic and the second agricultural        characteristic based on a value of the first agricultural        characteristic in the information map at the geographic location        and a value of the second agricultural characteristic sensed by        the in-situ sensor at the geographic location; and    -   a predictive map generator that generates a functional        predictive agricultural map of the field that maps predictive        values of the second agricultural characteristic to the        different geographic locations in the field based on the values        of the first agricultural characteristic in the information map        and based on the predictive agricultural model.    -   Example 2 is the agricultural work machine of any or all        previous examples, and further comprising:    -   a control system that generates control signals to control a        controllable subsystem on the agricultural work machine based on        the functional predictive agricultural map.    -   Example 3 is the agricultural work machine of any or all        previous examples, wherein the in-situ sensor comprises:    -   a tailings characteristic sensor that senses, as the second        agricultural characteristic, a characteristic of tailings in the        agricultural work machine.    -   Example 4 is the agricultural work machine of any or all        previous examples, wherein the in-situ sensor comprises:    -   a loss sensor that senses, as the second agricultural        characteristic, a characteristic indicative of crop loss from        the agricultural work machine.    -   Example 5 is the agricultural work machine of any or all        previous examples, wherein the in-situ sensor comprises:    -   a grain quality sensor that senses, as the second agricultural        characteristic, a characteristic indicative of grain quality in        the agricultural work machine.    -   Example 6 is the agricultural work machine of any or all        previous examples, wherein the in-situ sensor comprises:    -   an internal distribution sensor that senses, as the second        agricultural characteristic, a characteristics indicative of a        distribution of harvested material in the agricultural work        machine.    -   Example 7 is the agricultural work machine of any or all        previous examples, wherein the predictive map generator        comprises:    -   a tailings characteristic map generator that generates, as the        functional predictive agricultural map, a predictive tailings        characteristic map that maps, as the predictive values of the        second agricultural characteristic, predictive values of a        tailings characteristic to the different geographic locations in        the field based on the values of the first agricultural        characteristic in the information map and based on the        predictive agricultural model.    -   Example 8 is the agricultural work machine of any or all        previous examples, wherein the predictive map generator        comprises:    -   a loss map generator that generates, as the functional        predictive agricultural map, a predictive loss map that maps        predictive values of a crop loss characteristic to the different        geographic locations in the field based on the values of the        first agricultural characteristic in the information map and        based on the predictive agricultural model.    -   Example 9 is the agricultural work machine of any or all        previous examples, wherein the predictive map generator        comprises:    -   a grain quality map generator that generates, as the functional        predictive agricultural map, a predictive grain quality map that        maps predictive values of a grain quality characteristic to the        different geographic locations in the field based on the values        of the first agricultural characteristic in the information map        and based on the predictive agricultural model.    -   Example 10 is the agricultural work machine of any or all        previous examples, wherein the predictive map generator        comprises:    -   an internal distribution map generator that generates, as the        functional predictive agricultural map, a predictive internal        distribution map that maps predictive values of an internal        distribution characteristic, indicative of a characteristic of        processed material distribution in the agricultural work        machine, to the different geographic locations in the field        based on the values of the first agricultural characteristic in        the information map and based on the predictive agricultural        model.    -   Example 11 is the agricultural work machine of any or all        previous examples, wherein the communication system receives, as        the information map, a topographic map that includes, as the        first agricultural characteristic, a topographic characteristic,        wherein the predictive model generator generates the predictive        agricultural model to model a relationship between the        topographic characteristic and the second agricultural        characteristic.    -   Example 12 is the agricultural work machine of any or all        previous examples, wherein the communication system receives, as        the information map, a seed genotype map that includes as the        first agricultural characteristic, a seed genotype, wherein the        predictive model generator generates the predictive agricultural        model to model a relationship between the seed genotype and the        second agricultural characteristic.    -   Example 13 is the agricultural work machine of any or all        previous examples, wherein the communication system receives, as        the information map, a vegetative index map that includes, as        the first agricultural characteristic, a vegetative index        characteristic, wherein the predictive model generator generates        the predictive agricultural model to model a relationship        between the vegetative index characteristic and the second        agricultural characteristic.    -   Example 14 is the agricultural work machine of any or all        previous examples, wherein the communication system receives, as        the information map, a yield map that includes, as the first        agricultural characteristic, a predictive yield characteristic,        wherein the predictive model generator generates the predictive        agricultural model to model a relationship between the        predictive yield characteristic and the second agricultural        characteristic.    -   Example 15 is the agricultural work machine of any or all        previous examples, wherein the communication system receives, as        the information map, a biomass map that includes, as the first        agricultural characteristic, a biomass characteristic, wherein        the predictive model generator generates the predictive        agricultural model to model a relationship between the biomass        characteristic and the second agricultural characteristic.    -   Example 16 is the agricultural work machine of any or all        previous examples, wherein the communication system receives, as        the information map, a weed map that includes, as the first        agricultural characteristic, a weed characteristic, wherein the        predictive model generator generates the predictive agricultural        model to model a relationship between the weed characteristic        and the second agricultural characteristic.    -   Example 17 is a computer implemented method of generating a        functional predictive agricultural map, comprising:    -   receiving an information map, at an agricultural work machine,        that indicates values of a first agricultural characteristic        corresponding to different geographic locations in a field;    -   detecting a geographic location of the agricultural work        machine;    -   detecting, with an in-situ sensor, a second agricultural        characteristic indicative of a characteristic of processed        material corresponding to the geographic location;    -   generating a predictive agricultural model that models a        relationship between the first agricultural characteristic and        the second agricultural characteristic; and    -   controlling a predictive map generator to generate the        functional predictive agricultural map of the field that maps        predictive values of the second agricultural characteristic to        the different locations in the field based on the values of the        first agricultural characteristic in the information map and the        predictive agricultural model.    -   Example 18 is the computer implemented method of any or all        previous examples, and further comprising:    -   configuring the functional predictive agricultural map for a        control system that generates control signals to control a        controllable subsystem on the agricultural work machine based on        the functional predictive agricultural map.    -   Example 19 is an agricultural work machine, comprising:    -   a communication system that receives an information map that        includes values of a first agricultural characteristic        corresponding to different geographic locations in a field;    -   a geographic position sensor that detects a geographic location        of the agricultural work machine;    -   an in-situ sensor that detects a value of a second agricultural        characteristic indicative of a characteristic of processed        material corresponding to the geographic location;    -   a predictive model generator that generates a predictive        agricultural model that models a relationship between the first        agricultural characteristic and the second agricultural        characteristic based on a value of the first agricultural        characteristic in the information map at the geographic location        and a value of the second agricultural characteristic sensed by        the in-situ sensor at the geographic location; and    -   a predictive map generator that generates a functional        predictive agricultural map of the field that maps predictive        values of the second agricultural characteristic to the        different geographic locations in the field based on the values        of the first agricultural characteristic in the information map        and based on the predictive agricultural model, the predictive        map generator configuring the functional predictive agricultural        map for a control system that generates control signals to        control a controllable subsystem on the agricultural work        machine based on the functional predictive agricultural map.    -   Example 20 is the agricultural work machine of any or all        previous examples, wherein the in-situ sensor comprises one or        more of:    -   a tailings characteristic sensor that senses a characteristic of        tailings in the agricultural work machine as the second        agricultural characteristic;    -   a loss sensor that senses a characteristic indicative of crop        loss from the agricultural work machine as the second        agricultural characteristic;    -   a grain quality sensor that senses a characteristic indicative        of grain quality in the agricultural work machine as the second        agricultural characteristic; and    -   an internal distribution sensor that senses a characteristics        indicative of a distribution of harvested material in the        agricultural work machine as the second agricultural        characteristic.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of the claims.

What is claimed is:
 1. An agricultural system comprising: acommunication system that receives an information map that includesvalues of a first agricultural characteristic corresponding to differentgeographic locations in a field; a geographic position sensor thatdetects a geographic location of an agricultural work machine; anin-situ sensor that detects a value of a second agriculturalcharacteristic indicative of a characteristic of processed materialcorresponding to the geographic location; a predictive model generatorthat generates a predictive agricultural model that models arelationship between the first agricultural characteristic and thesecond agricultural characteristic based on a value of the firstagricultural characteristic in the information map at the geographiclocation and a value of the second agricultural characteristic sensed bythe in-situ sensor at the geographic location; and a predictive mapgenerator that generates a functional predictive agricultural map of thefield that maps predictive values of the second agriculturalcharacteristic to the different geographic locations in the field basedon the values of the first agricultural characteristic in theinformation map and based on the predictive agricultural model.
 2. Theagricultural system of claim 1, and further comprising: a control systemthat generates control signals to control a controllable subsystem onthe agricultural work machine based on the functional predictiveagricultural map.
 3. The agricultural system of claim 1, wherein thein-situ sensor comprises: a tailings characteristic sensor that senses,as the second agricultural characteristic, a characteristic of tailingsin the agricultural work machine.
 4. The agricultural system of claim 1,wherein the in-situ sensor comprises: a loss sensor that senses, as thesecond agricultural characteristic, a characteristic indicative of croploss from the agricultural work machine.
 5. The agricultural system ofclaim 1, wherein the in-situ sensor comprises: a grain quality sensorthat senses, as the second agricultural characteristic, a characteristicindicative of grain quality in the agricultural work machine.
 6. Theagricultural system of claim 1, wherein the in-situ sensor comprises: aninternal distribution sensor that senses, as the second agriculturalcharacteristic, a characteristics indicative of a distribution ofharvested material in the agricultural work machine.
 7. The agriculturalsystem of claim 1, wherein the predictive map generator comprises: atailings characteristic map generator that generates, as the functionalpredictive agricultural map, a predictive tailings characteristic mapthat maps, as the predictive values of the second agriculturalcharacteristic, predictive values of a tailings characteristic to thedifferent geographic locations in the field based on the values of thefirst agricultural characteristic in the information map and based onthe predictive agricultural model.
 8. The agricultural system of claim1, wherein the predictive map generator comprises: a loss map generatorthat generates, as the functional predictive agricultural map, apredictive loss map that maps predictive values of a crop losscharacteristic to the different geographic locations in the field basedon the values of the first agricultural characteristic in theinformation map and based on the predictive agricultural model.
 9. Theagricultural system of claim 1, wherein the predictive map generatorcomprises: a grain quality map generator that generates, as thefunctional predictive agricultural map, a predictive grain quality mapthat maps predictive values of a grain quality characteristic to thedifferent geographic locations in the field based on the values of thefirst agricultural characteristic in the information map and based onthe predictive agricultural model.
 10. The agricultural system of claim1, wherein the predictive map generator comprises: an internaldistribution map generator that generates, as the functional predictiveagricultural map, a predictive internal distribution map that mapspredictive values of an internal distribution characteristic, indicativeof a characteristic of processed material distribution in theagricultural work machine, to the different geographic locations in thefield based on the values of the first agricultural characteristic inthe information map and based on the predictive agricultural model. 11.The agricultural system of claim 1, wherein the communication systemreceives, as the information map, a topographic map that includes, asthe first agricultural characteristic, a topographic characteristic,wherein the predictive model generator generates the predictiveagricultural model to model a relationship between the topographiccharacteristic and the second agricultural characteristic.
 12. Theagricultural system of claim 1, wherein the communication systemreceives, as the information map, a seed genotype map that includes asthe first agricultural characteristic, a seed genotype, wherein thepredictive model generator generates the predictive agricultural modelto model a relationship between the seed genotype and the secondagricultural characteristic.
 13. The agricultural system of claim 1,wherein the communication system receives, as the information map, avegetative index map that includes, as the first agriculturalcharacteristic, a vegetative index characteristic, wherein thepredictive model generator generates the predictive agricultural modelto model a relationship between the vegetative index characteristic andthe second agricultural characteristic.
 14. The agricultural system ofclaim 1, wherein the communication system receives, as the informationmap, a yield map that includes, as the first agriculturalcharacteristic, a predictive yield characteristic, wherein thepredictive model generator generates the predictive agricultural modelto model a relationship between the predictive yield characteristic andthe second agricultural characteristic.
 15. The agricultural system ofclaim 1, wherein the communication system receives, as the informationmap, a biomass map that includes, as the first agriculturalcharacteristic, a biomass characteristic, wherein the predictive modelgenerator generates the predictive agricultural model to model arelationship between the biomass characteristic and the secondagricultural characteristic.
 16. The agricultural system of claim 1,wherein the communication system receives, as the information map, aweed map that includes, as the first agricultural characteristic, a weedcharacteristic, wherein the predictive model generator generates thepredictive agricultural model to model a relationship between the weedcharacteristic and the second agricultural characteristic.
 17. Acomputer implemented method of generating a functional predictiveagricultural map, comprising: receiving an information map thatindicates values of a first agricultural characteristic corresponding todifferent geographic locations in a field; detecting a geographiclocation of an agricultural work machine; detecting, with an in-situsensor, a second agricultural characteristic indicative of acharacteristic of processed material corresponding to the geographiclocation; generating a predictive agricultural model that models arelationship between the first agricultural characteristic and thesecond agricultural characteristic; and controlling a predictive mapgenerator to generate the functional predictive agricultural map of thefield that maps predictive values of the second agriculturalcharacteristic to the different locations in the field based on thevalues of the first agricultural characteristic in the information mapand the predictive agricultural model.
 18. The computer implementedmethod of claim 17, and further comprising: configuring the functionalpredictive agricultural map for a control system that generates controlsignals to control a controllable subsystem on the agricultural workmachine based on the functional predictive agricultural map.
 19. Anagricultural system comprising: a communication system that receives aninformation map that includes values of a first agriculturalcharacteristic corresponding to different geographic locations in afield; a geographic position sensor that detects a geographic locationof an agricultural work machine; an in-situ sensor that detects a valueof a second agricultural characteristic indicative of a characteristicof processed material corresponding to the geographic location; apredictive model generator that generates a predictive agriculturalmodel that models a relationship between the first agriculturalcharacteristic and the second agricultural characteristic based on avalue of the first agricultural characteristic in the information map atthe geographic location and a value of the second agriculturalcharacteristic sensed by the in-situ sensor at the geographic location;and a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive values of the secondagricultural characteristic to the different geographic locations in thefield based on the values of the first agricultural characteristic inthe information map and based on the predictive agricultural model, thepredictive map generator configuring the functional predictiveagricultural map for a control system that generates control signals tocontrol a controllable subsystem on the agricultural work machine basedon the functional predictive agricultural map.
 20. The agriculturalsystem of claim 19, wherein the in-situ sensor comprises one or more of:a tailings characteristic sensor that senses a characteristic oftailings in the agricultural work machine as the second agriculturalcharacteristic; a loss sensory that senses a characteristic indicativeof crop loss from the agricultural work machine as the secondagricultural characteristic; a grain quality sensory that senses acharacteristic indicative of grain quality in the agricultural workmachine as the second agricultural characteristic; and an internaldistribution sensor that senses a characteristics indicative of thedistribution of harvested material in the agricultural work machine asthe second agricultural characteristic.